Lab Publications
2020 |
M U Saleh; C Deline; S Kingston; N K T Jayakumar; E Benoit; J B Harley; C Furse; M Scarpulla Detection and Localization of Disconnections in PV Strings Using Spread-Spectrum Time-Domain Reflectometry Journal Article IEEE Journal of Photovoltaics, 10 (1), pp. 236-242, 2020. @article{Harley2020, title = {Detection and Localization of Disconnections in PV Strings Using Spread-Spectrum Time-Domain Reflectometry}, author = {M U Saleh and C Deline and S Kingston and N K T Jayakumar and E Benoit and J B Harley and C Furse and M Scarpulla}, year = {2020}, date = {2020-01-01}, journal = {IEEE Journal of Photovoltaics}, volume = {10}, number = {1}, pages = {236-242}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
M U Saleh; J B Harley; N K T Jayakumar; S Kingston; E Benoit; M Scarpulla; C Furse Reflectometry on asymmetric transmission line systems Journal Article Progress In Electromagnetics Research M, 89 , pp. 121-130, 2020. @article{Harley2020b, title = {Reflectometry on asymmetric transmission line systems}, author = {M U Saleh and J B Harley and N K T Jayakumar and S Kingston and E Benoit and M Scarpulla and C Furse}, year = {2020}, date = {2020-01-01}, journal = {Progress In Electromagnetics Research M}, volume = {89}, pages = {121-130}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
A C S Douglass; D Sparkman; J B Harley Segmentation of Hidden Delaminations with Pitch?Catch Ultrasonic Testing and Agglomerative Clustering Journal Article Journal of Nondestructive Evaluation, 39 (1), 2020. @article{Harley2020c, title = {Segmentation of Hidden Delaminations with Pitch?Catch Ultrasonic Testing and Agglomerative Clustering}, author = {A C S Douglass and D Sparkman and J B Harley}, year = {2020}, date = {2020-01-01}, journal = {Journal of Nondestructive Evaluation}, volume = {39}, number = {1}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
S Sabeti; J B Harley Spatio-temporal undersampling: Recovering ultrasonic guided wavefields from incomplete data with compressive sensing Journal Article Mechanical Systems and Signal Processing, 140 , 2020. @article{Harley2020d, title = {Spatio-temporal undersampling: Recovering ultrasonic guided wavefields from incomplete data with compressive sensing}, author = {S Sabeti and J B Harley}, year = {2020}, date = {2020-01-01}, journal = {Mechanical Systems and Signal Processing}, volume = {140}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Joel B Harley Spread Spectrum Time Domain Reflectometry with Lumped Elements on Asymmetric Transmission Lines Journal Article IEEE Sensors Journal, 2020, ISSN: 1530-437X. @article{Joel_B._Harley70291353, title = {Spread Spectrum Time Domain Reflectometry with Lumped Elements on Asymmetric Transmission Lines}, author = {Joel B Harley}, url = {http://doi.org/10.1109/jsen.2020.2967894}, doi = {10.1109/jsen.2020.2967894}, issn = {1530-437X}, year = {2020}, date = {2020-01-01}, journal = {IEEE Sensors Journal}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2019 |
Alexander C S Douglass; Daniel O Adams; Chris Deemer; Joel B Harley Singular Value-based damage statistics for guided wave Structural Health monitoring Inproceedings Proc. of the Review of Quantitative Nondestructive Evaluation, pp. 050014, Ämerican Institute of Physics, 2019. @inproceedings{Douglass2019-jn, title = {Singular Value-based damage statistics for guided wave Structural Health monitoring}, author = {Alexander C S Douglass and Daniel O Adams and Chris Deemer and Joel B Harley}, doi = {10.1063/1.5099780}, year = {2019}, date = {2019-05-01}, booktitle = {Proc. of the Review of Quantitative Nondestructive Evaluation}, volume = {2102}, pages = {050014}, publisher = {Ämerican Institute of Physics}, abstract = {Guided wave structural health monitoring is used to inspect large structures with ultrasonic waves. To detect damage, statistics are typically computed from raw guided wave responses. Many current detection methods use single-signal statistics and batch statistics [1]. Single-signal statistics are derived from a single measurement while batch statistics originate from time histories. Singular value decomposition (a batch approach) compared singular vectors with a step function to identify rapid changes in the data (such as damage). While effective, this approach is time consuming and computationally expensive. In this paper, we study two damage statistics derived from singular values, rather than singular vectors, that reduce computationally complexity. We refer to these statistics as normalized singular statistics and Anderson statistics. As a preliminary statistical study, Monte Carlo simulations were used to create guided wave data with random white, Gaussian noise. We demonstrate that that the Anderson statistic is has greater consistency as measurements are added, robustness to noise, and damage sensitivity.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Guided wave structural health monitoring is used to inspect large structures with ultrasonic waves. To detect damage, statistics are typically computed from raw guided wave responses. Many current detection methods use single-signal statistics and batch statistics [1]. Single-signal statistics are derived from a single measurement while batch statistics originate from time histories. Singular value decomposition (a batch approach) compared singular vectors with a step function to identify rapid changes in the data (such as damage). While effective, this approach is time consuming and computationally expensive. In this paper, we study two damage statistics derived from singular values, rather than singular vectors, that reduce computationally complexity. We refer to these statistics as normalized singular statistics and Anderson statistics. As a preliminary statistical study, Monte Carlo simulations were used to create guided wave data with random white, Gaussian noise. We demonstrate that that the Anderson statistic is has greater consistency as measurements are added, robustness to noise, and damage sensitivity. |
Soroosh Sabeti; Joel B Harley Polar sparse wavenumber analysis for guided wave reconstruction Inproceedings Proc. of the Review of Quantitative Nondestructive Evaluation, pp. 050012, Ämerican Institute of Physics, 2019. @inproceedings{Sabeti2019-kw, title = {Polar sparse wavenumber analysis for guided wave reconstruction}, author = {Soroosh Sabeti and Joel B Harley}, doi = {10.1063/1.5099778}, year = {2019}, date = {2019-05-01}, booktitle = {Proc. of the Review of Quantitative Nondestructive Evaluation}, volume = {2102}, pages = {050012}, publisher = {Ämerican Institute of Physics}, abstract = {In non-destructive evaluation/testing (NDE/NDT) and structural health monitoring (SHM) applications, guided waves are commonly employed and widely studied. Wave behavior characterization and analysis can be vital in determining the state of the structure under inspection. Effective analysis of guided waves, however, is encumbered by their intricate nature. This intricacy is further aggravated in structures with anisotropic characteristics. Moreover, the data acquisition process can be costly and time-consuming. Therefore, it is significant to achieve behavior prediction of guided waves from limited measurements. To make this possible, compressive sensing based methodologies and predictive models have been presented in the literature. Specifically in prior work, a two-dimensional sparse wavenumber analysis (2D-SWA) framework was introduced to model anisotropic wave propagation. In this paper, we present a similar framework whereby a sparser representation of guided waves can be obtained by incorporating information of the measurements in polar coordinates. We implement this method, which we refer to as polar sparse wavenumber analysis (PSWA), on a simulated wavefield propagating in a composite material and demonstrate how it is capable of accurately reconstructing the entire wavefield from a few spatial measurements.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In non-destructive evaluation/testing (NDE/NDT) and structural health monitoring (SHM) applications, guided waves are commonly employed and widely studied. Wave behavior characterization and analysis can be vital in determining the state of the structure under inspection. Effective analysis of guided waves, however, is encumbered by their intricate nature. This intricacy is further aggravated in structures with anisotropic characteristics. Moreover, the data acquisition process can be costly and time-consuming. Therefore, it is significant to achieve behavior prediction of guided waves from limited measurements. To make this possible, compressive sensing based methodologies and predictive models have been presented in the literature. Specifically in prior work, a two-dimensional sparse wavenumber analysis (2D-SWA) framework was introduced to model anisotropic wave propagation. In this paper, we present a similar framework whereby a sparser representation of guided waves can be obtained by incorporating information of the measurements in polar coordinates. We implement this method, which we refer to as polar sparse wavenumber analysis (PSWA), on a simulated wavefield propagating in a composite material and demonstrate how it is capable of accurately reconstructing the entire wavefield from a few spatial measurements. |
Yi Tang; Samuel Brown; Jeff Sorensen; Joel B Harley Reduced Rank Least Squares for Real-Time Short Term Estimation of Mean Arterial Blood Pressure in Septic Patients Receiving Norepinephrine Journal Article IEEE Journal of Translational Engineering in Health and Medicine, 7 , pp. 1–9, 2019. @article{Tang2019-df, title = {Reduced Rank Least Squares for Real-Time Short Term Estimation of Mean Arterial Blood Pressure in Septic Patients Receiving Norepinephrine}, author = {Yi Tang and Samuel Brown and Jeff Sorensen and Joel B Harley}, doi = {10.1109/JTEHM.2019.2919020}, year = {2019}, date = {2019-01-01}, journal = {IEEE Journal of Translational Engineering in Health and Medicine}, volume = {7}, pages = {1--9}, abstract = {Norepinephrine (NE), an endogenous catecholamine, is a mainstay treatment for septic shock, which is a life-threatening manifestation of severe infection. NE counteracts the loss in blood pressure associated with septic shock. However, an NE infusion that is too low fails to counteract the blood pressure drop, and an NE infusion that is too high can cause a hypertensive crisis and heart attack. Ideally, the NE infusion rate should maintain a patient's mean arterial blood pressure (MAP) above 65 mmHg. There are a few data-driven, quantitative models to predict the MAP, and incorporate NE effects. This paper presents a model, driven by intensive care unit (ICU) measurable data and known NE inputs, to predict the future MAP of an ICU patient. We derive a least square estimation model for MAP based on available ICU data, including heart period, NE infusion rate, and respiration wave. We learn the parameters of our model from initial patient data and then use this information to predict future MAP data. We assess our model with data from 12 septic patients. Our model successfully predicts and tracks MAP when the NE infusion rate changes. Specifically, we predict MAP 3 to 20 min in the future with the mean error of less than 4 to 7 mmHg over 12 patients. Conclusion: this new approach creates the potential to advance methods for predicting NE infusion rate in septic patients. Significance: successfully predicted patients' MAP could reduce catastrophic human error and lessen clinicians' workload.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Norepinephrine (NE), an endogenous catecholamine, is a mainstay treatment for septic shock, which is a life-threatening manifestation of severe infection. NE counteracts the loss in blood pressure associated with septic shock. However, an NE infusion that is too low fails to counteract the blood pressure drop, and an NE infusion that is too high can cause a hypertensive crisis and heart attack. Ideally, the NE infusion rate should maintain a patient's mean arterial blood pressure (MAP) above 65 mmHg. There are a few data-driven, quantitative models to predict the MAP, and incorporate NE effects. This paper presents a model, driven by intensive care unit (ICU) measurable data and known NE inputs, to predict the future MAP of an ICU patient. We derive a least square estimation model for MAP based on available ICU data, including heart period, NE infusion rate, and respiration wave. We learn the parameters of our model from initial patient data and then use this information to predict future MAP data. We assess our model with data from 12 septic patients. Our model successfully predicts and tracks MAP when the NE infusion rate changes. Specifically, we predict MAP 3 to 20 min in the future with the mean error of less than 4 to 7 mmHg over 12 patients. Conclusion: this new approach creates the potential to advance methods for predicting NE infusion rate in septic patients. Significance: successfully predicted patients' MAP could reduce catastrophic human error and lessen clinicians' workload. |
Mashad U Saleh; Josiah LaCombe; Naveen K T Jayakumar; Samuel Kingston; Joel B Harley; Cynthia Furse; Michael Scarpulla Signal Propagation Through Piecewise Transmission Lines for Interpretation of Reflectometry in Photovoltaic Systems Journal Article IEEE Journal of Photovoltaics, 9 (2), pp. 506–512, 2019. @article{Saleh2019-la, title = {Signal Propagation Through Piecewise Transmission Lines for Interpretation of Reflectometry in Photovoltaic Systems}, author = {Mashad U Saleh and Josiah LaCombe and Naveen K T Jayakumar and Samuel Kingston and Joel B Harley and Cynthia Furse and Michael Scarpulla}, doi = {10.1109/JPHOTOV.2018.2884011}, year = {2019}, date = {2019-01-01}, journal = {IEEE Journal of Photovoltaics}, volume = {9}, number = {2}, pages = {506--512}, abstract = {We present a framework for analyzing electromagnetic signal propagation through piecewise-defined transmission lines with arbitrary, series-connected impedances. While the formulation is general and scalable, we apply it here to propagation through a photovoltaic module with cables on either side acting, with a home run cable, as a section of an inhomogeneous transmission line. Understanding propagation through this unit of a series-connected string of photovoltaic modules is necessary to enable the use of time-domain reflectometry techniques for monitoring the status of individual components in series-connected strings within large photovoltaic arrays.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We present a framework for analyzing electromagnetic signal propagation through piecewise-defined transmission lines with arbitrary, series-connected impedances. While the formulation is general and scalable, we apply it here to propagation through a photovoltaic module with cables on either side acting, with a home run cable, as a section of an inhomogeneous transmission line. Understanding propagation through this unit of a series-connected string of photovoltaic modules is necessary to enable the use of time-domain reflectometry techniques for monitoring the status of individual components in series-connected strings within large photovoltaic arrays. |
Soroosh Sabeti; Cara A C Leckey; Luca De Marchi; Joel B Harley Sparse Wavenumber Recovery and Prediction of Anisotropic Guided Waves in Composites: A Comparative Study Journal Article IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 2019. @article{Sabeti2019-rt, title = {Sparse Wavenumber Recovery and Prediction of Anisotropic Guided Waves in Composites: A Comparative Study}, author = {Soroosh Sabeti and Cara A C Leckey and Luca De Marchi and Joel B Harley}, doi = {10.1109/TUFFC.2019.2918746}, year = {2019}, date = {2019-01-01}, journal = {IEEE Trans. Ultrason. Ferroelectr. Freq. Control}, abstract = {Guided wave methodologies are among the established approaches for structural health monitoring. For guided wave data, being able to accurately estimate wave properties in the absence of ample measurements can greatly facilitate the often time-consuming and potentially expensive data acquisition procedure. Nevertheless, inherent complexities of the guided waves, including their multi-modal and frequency dispersive nature, hinder processing, analysis, and behavior prediction. The severity of these complexities is even higher in anisotropic media, such as composites. Several methods, including sparse wavenumber analysis, have been proposed in the literature to characterize guided wave propagation by extracting wave characteristics in a particular medium from the information contained in a few measurements, and subsequently using this information for full wavefield prediction. In this paper, we investigate the efficacy of guided wave reconstruction techniques, based on sparse wavenumber analysis, for predicting the behavior of guided waves in composite materials. We implement these techniques on several experimental and simulation datasets. We study their performance in estimating the frequency-dependent (dispersive) and anisotropic velocities of guided waves and in reconstructing full wavefields from limited available information.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Guided wave methodologies are among the established approaches for structural health monitoring. For guided wave data, being able to accurately estimate wave properties in the absence of ample measurements can greatly facilitate the often time-consuming and potentially expensive data acquisition procedure. Nevertheless, inherent complexities of the guided waves, including their multi-modal and frequency dispersive nature, hinder processing, analysis, and behavior prediction. The severity of these complexities is even higher in anisotropic media, such as composites. Several methods, including sparse wavenumber analysis, have been proposed in the literature to characterize guided wave propagation by extracting wave characteristics in a particular medium from the information contained in a few measurements, and subsequently using this information for full wavefield prediction. In this paper, we investigate the efficacy of guided wave reconstruction techniques, based on sparse wavenumber analysis, for predicting the behavior of guided waves in composite materials. We implement these techniques on several experimental and simulation datasets. We study their performance in estimating the frequency-dependent (dispersive) and anisotropic velocities of guided waves and in reconstructing full wavefields from limited available information. |
Samuel Kingston; Evan Benoit; Naveen K T Jayakumar; Mashad U Saleh; Josiah LaCombe; Cynthia M Furse; Michael A Scarpulla; Joel B Harley Spread spectrum time-domain reflectometry for detecting and locating capacitive impedances Inproceedings Proc. of the Review of Quantitative Nondestructive Evaluation, pp. 090009, 2019. @inproceedings{Kingston2019-vo, title = {Spread spectrum time-domain reflectometry for detecting and locating capacitive impedances}, author = {Samuel Kingston and Evan Benoit and Naveen K T Jayakumar and Mashad U Saleh and Josiah LaCombe and Cynthia M Furse and Michael A Scarpulla and Joel B Harley}, doi = {10.1063/1.5099827}, year = {2019}, date = {2019-01-01}, booktitle = {Proc. of the Review of Quantitative Nondestructive Evaluation}, volume = {2102}, pages = {090009}, abstract = {This paper describes new algorithms for spread spectrum time domain reflectometry (SSTDR) for detecting and locating faults in photovoltaic (PV) panels. Specifically, we present a new method for identifying the impedance of multiple loads (such as PV panels) on a single transmission line. This method is based on adapting a well-known algorithm, known as orthogonal matching pursuit, to match simulated waveforms with multiple reflections in an SSTDR waveform. We demonstrate that our method successfully extracts waveform data from the SSTDR experiments and correctly estimates load impedances corresponding to reflections in this waveform data. We experimentally demonstrate that our method correctly estimates the capacitance values of various capacitive loads on a transmission line with an error as low as4%.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper describes new algorithms for spread spectrum time domain reflectometry (SSTDR) for detecting and locating faults in photovoltaic (PV) panels. Specifically, we present a new method for identifying the impedance of multiple loads (such as PV panels) on a single transmission line. This method is based on adapting a well-known algorithm, known as orthogonal matching pursuit, to match simulated waveforms with multiple reflections in an SSTDR waveform. We demonstrate that our method successfully extracts waveform data from the SSTDR experiments and correctly estimates load impedances corresponding to reflections in this waveform data. We experimentally demonstrate that our method correctly estimates the capacitance values of various capacitive loads on a transmission line with an error as low as4%. |
Naveen K T Jayakumar; Evan Benoit; Samuel Kingston; Mashad U Saleh; Michael Scarpulla; J B Harley; Cynthia Furse Postprocessing for Improved Accuracy and Resolution of Spread Spectrum Time-Domain Reflectometry Journal Article IEEE Sensors Letters, 3 (6), pp. 1–4, 2019. @article{Jayakumar2019-hc, title = {Postprocessing for Improved Accuracy and Resolution of Spread Spectrum Time-Domain Reflectometry}, author = {Naveen K T Jayakumar and Evan Benoit and Samuel Kingston and Mashad U Saleh and Michael Scarpulla and J B Harley and Cynthia Furse}, doi = {10.1109/LSENS.2019.2916636}, year = {2019}, date = {2019-01-01}, journal = {IEEE Sensors Letters}, volume = {3}, number = {6}, pages = {1--4}, abstract = {Reflectometry, which is commonly used for locating faults on electrical wires, produces sampled time domain signatures with peaks that are often missed due to this sampling. Resultant errors in these sampled peaks translate to errors in calculating the impedance and location of the fault. Typical signal processing methods to improve the accuracy of these sampled peaks have complexity on the order of O(N2). For embedded fault location applications, algorithms with lower complexity are desired. In this article, we introduce three algorithms for improving the accuracy of the peak with a complexity of O(N). We evaluate these algorithms on the practical case of calculating the velocity of propagation and the characteristic impedance of a photovoltaic (PV) cable using spread spectrum time-domain reflectometry.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Reflectometry, which is commonly used for locating faults on electrical wires, produces sampled time domain signatures with peaks that are often missed due to this sampling. Resultant errors in these sampled peaks translate to errors in calculating the impedance and location of the fault. Typical signal processing methods to improve the accuracy of these sampled peaks have complexity on the order of O(N2). For embedded fault location applications, algorithms with lower complexity are desired. In this article, we introduce three algorithms for improving the accuracy of the peak with a complexity of O(N). We evaluate these algorithms on the practical case of calculating the velocity of propagation and the characteristic impedance of a photovoltaic (PV) cable using spread spectrum time-domain reflectometry. |
K Supreet Alguri; Joel B Harley Transfer learning of ultrasonic guided waves using autoencoders: A preliminary study Inproceedings Proc. of the Review of Quantitative Nondestructive Evaluation, pp. 050013, 2019. @inproceedings{Alguri2019-km, title = {Transfer learning of ultrasonic guided waves using autoencoders: A preliminary study}, author = {K Supreet Alguri and Joel B Harley}, doi = {10.1063/1.5099779}, year = {2019}, date = {2019-01-01}, booktitle = {Proc. of the Review of Quantitative Nondestructive Evaluation}, volume = {2102}, pages = {050013}, abstract = {In recent years, the use of scanning laser Doppler vibrometery and full wavefield acquisition has grown to aid the char- acterization of ultrasonic waves and the detection of structural defects. Yet, these methods require a considerable amount of time to acquire full wavefield data. Therefore, there is a significant need to reduce acquisition time. In this preliminary work, we present a transfer learning approach for reducing the number of sampled measurements necessary. Our method utilizes numerical simula- tions, combined with a small number of spatially sampled random measurements from an experimental structure, to reconstruct full wavefield data. Specifically, we use an autoencoder neural network to learn low-dimensional representations of wave propagation from numerical simulations. We then input a few experimental measurements into the neural network to reconstruct full wave- field data. To demonstrate the ability of our framework, we show our initial success in three scenarios. We show reconstruction accuracies of 86% with one-fourth of the total measurements.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In recent years, the use of scanning laser Doppler vibrometery and full wavefield acquisition has grown to aid the char- acterization of ultrasonic waves and the detection of structural defects. Yet, these methods require a considerable amount of time to acquire full wavefield data. Therefore, there is a significant need to reduce acquisition time. In this preliminary work, we present a transfer learning approach for reducing the number of sampled measurements necessary. Our method utilizes numerical simula- tions, combined with a small number of spatially sampled random measurements from an experimental structure, to reconstruct full wavefield data. Specifically, we use an autoencoder neural network to learn low-dimensional representations of wave propagation from numerical simulations. We then input a few experimental measurements into the neural network to reconstruct full wave- field data. To demonstrate the ability of our framework, we show our initial success in three scenarios. We show reconstruction accuracies of 86% with one-fourth of the total measurements. |
J B Harley; M U Saleh; S Kingston; M A Scarpulla; C M Furse Fast transient simulations for multi-segment transmission lines with a graphical model Journal Article Progress in Electromagnetics Research, 165 , pp. 67-82, 2019. @article{Harley2019b, title = {Fast transient simulations for multi-segment transmission lines with a graphical model}, author = {J B Harley and M U Saleh and S Kingston and M A Scarpulla and C M Furse}, year = {2019}, date = {2019-01-01}, journal = {Progress in Electromagnetics Research}, volume = {165}, pages = {67-82}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
E Benoit; N K T Jayakumar; S Kingston; M U Saleh; M Scarpulla; J Harley; C Furse Applicability of SSTDR analysis of complex loads Inproceedings pp. 2087-2088, 2019. @inproceedings{Harley2019, title = {Applicability of SSTDR analysis of complex loads}, author = {E Benoit and N K T Jayakumar and S Kingston and M U Saleh and M Scarpulla and J Harley and C Furse}, year = {2019}, date = {2019-01-01}, journal = {2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2019 - Proceedings}, pages = {2087-2088}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
J B Harley; D Sparkman Machine learning and NDE: Past, present, and future Inproceedings 2019. @inproceedings{Harley2019c, title = {Machine learning and NDE: Past, present, and future}, author = {J B Harley and D Sparkman}, year = {2019}, date = {2019-01-01}, journal = {AIP Conference Proceedings}, volume = {2102}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
A C S Douglass; D O Adams; C Deemer; J B Harley Singular Value-based damage statistics for guided wave Structural Health monitoring Journal Article AIP Conference Proceedings, 2102 , 2019. @article{Harley2019g, title = {Singular Value-based damage statistics for guided wave Structural Health monitoring}, author = {A C S Douglass and D O Adams and C Deemer and J B Harley}, year = {2019}, date = {2019-01-01}, journal = {AIP Conference Proceedings}, volume = {2102}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
S Sabeti; C A C Leckey; L De Marchi; J B Harley Sparse wavenumber recovery and prediction of anisotropic guided waves in composites: A comparative study Journal Article IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 66 (8), pp. 1352-1363, 2019. @article{Harley2019h, title = {Sparse wavenumber recovery and prediction of anisotropic guided waves in composites: A comparative study}, author = {S Sabeti and C A C Leckey and L De Marchi and J B Harley}, year = {2019}, date = {2019-01-01}, journal = {IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control}, volume = {66}, number = {8}, pages = {1352-1363}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
S Kingston; N K T Jayakumar; M U Saleh; A Edun; E Benoit; R Sun; C M Furse; M A Scarpulla; J B Harley Spread Spectrum Time Domain Reflectometry (SSTDR) and Dictionary Matching to Measure Capacitance for PV cells Journal Article AUTOTESTCON (Proceedings), 2019-January , 2019. @article{Harley2019i, title = {Spread Spectrum Time Domain Reflectometry (SSTDR) and Dictionary Matching to Measure Capacitance for PV cells}, author = {S Kingston and N K T Jayakumar and M U Saleh and A Edun and E Benoit and R Sun and C M Furse and M A Scarpulla and J B Harley}, year = {2019}, date = {2019-01-01}, journal = {AUTOTESTCON (Proceedings)}, volume = {2019-January}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2018 |
Daniel J Alabi Mehrdad Ramezani Joel B Harley Roozbeh Tabrizian Characterizing Micro- and Nano-Materials Based on Their Ultrasonic Dispersion Properties: A Feasibility Study Inproceedings Proc. of the IEEE International Ultrasonics Symposium, pp. 206–212, 2018. @inproceedings{Alabi2018-qt, title = {Characterizing Micro- and Nano-Materials Based on Their Ultrasonic Dispersion Properties: A Feasibility Study}, author = {Daniel J Alabi Mehrdad Ramezani Joel B Harley Roozbeh Tabrizian}, doi = {10.1109/ULTSYM.2018.8579809}, year = {2018}, date = {2018-10-01}, booktitle = {Proc. of the IEEE International Ultrasonics Symposium}, pages = {206--212}, abstract = {Current methods for characterizing micro- and nano-scale materials (typically based on laser spectroscopy and xray diffraction) are expensive, slow, bulky, and cannot be applied in situ. In contrast with these approaches, this paper explores the use of ultrasonic transduction as an inexpensive tool for in situ material characterization at the micro- and nano-scale. Specifically, we present a technique for characterizing materials based on the analysis of ultrasonic dispersion (i.e., the frequency-dependent wavenumbers) of thin films. Through experimental and simulation results, we perform our technique with silicon and a piezoelectric stack consisting of silicon, molybdenum and aluminum nitride. Simulations are performed in COMSOL Multiphysics. Experimental data is collected from a piezoelectrically transduced nano-acoustic waveguide using a digital holographic microscope, which enables high-resolution measurement of displacement vector over extended regions. Our results demonstrate a near-match between the experimental data and simulations, validating our proposed characterization methodology.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Current methods for characterizing micro- and nano-scale materials (typically based on laser spectroscopy and xray diffraction) are expensive, slow, bulky, and cannot be applied in situ. In contrast with these approaches, this paper explores the use of ultrasonic transduction as an inexpensive tool for in situ material characterization at the micro- and nano-scale. Specifically, we present a technique for characterizing materials based on the analysis of ultrasonic dispersion (i.e., the frequency-dependent wavenumbers) of thin films. Through experimental and simulation results, we perform our technique with silicon and a piezoelectric stack consisting of silicon, molybdenum and aluminum nitride. Simulations are performed in COMSOL Multiphysics. Experimental data is collected from a piezoelectrically transduced nano-acoustic waveguide using a digital holographic microscope, which enables high-resolution measurement of displacement vector over extended regions. Our results demonstrate a near-match between the experimental data and simulations, validating our proposed characterization methodology. |
Alexander C S Douglass; Joel B Harley Flexible, multi-measurement guided wave damage detection under varying temperatures Inproceedings Proc. of the Review of Quantitative Nondestructive Evaluation, pp. 230008, American Institute of Physics, 2018. @inproceedings{Douglass2018-qp, title = {Flexible, multi-measurement guided wave damage detection under varying temperatures}, author = {Alexander C S Douglass and Joel B Harley}, doi = {10.1063/1.5031655}, year = {2018}, date = {2018-04-01}, booktitle = {Proc. of the Review of Quantitative Nondestructive Evaluation}, volume = {1949}, pages = {230008}, publisher = {American Institute of Physics}, abstract = {Temperature compensation in structural health monitoring helps identify damage in a structure by removing data variations due to environmental conditions, such as temperature. Stretch-based methods are one of the most commonly used temperature compensation methods. To account for variations in temperature, stretch-based methods optimally stretch signals in time to optimally match a measurement to a baseline. All of the data is then compared with the single baseline to determine the presence of damage. Yet, for these methods to be effective, the measurement and the baseline must satisfy the inherent assumptions of the temperature compensation method. In many scenarios, these assumptions are wrong, the methods generate error, and damage detection fails. To improve damage detection, a multi-measurement damage detection method is introduced. By using each measurement in the dataset as a baseline, error caused by imperfect temperature compensation is reduced. The multi-measurement method increases the detection effectiveness of our damage metric, or damage indicator, over time and reduces the presence of additional peaks caused by temperature that could be mistaken for damage. By using many baselines, the variance of the damage indicator is reduced and the effects from damage are amplified. Notably, the multi-measurement improves damage detection over single-measurement methods. This is demonstrated through an increase in the maximum of our damage signature from 0.55 to 0.95 (where large values, up to a maximum of one, represent a statistically significant change in the data due to damage).}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Temperature compensation in structural health monitoring helps identify damage in a structure by removing data variations due to environmental conditions, such as temperature. Stretch-based methods are one of the most commonly used temperature compensation methods. To account for variations in temperature, stretch-based methods optimally stretch signals in time to optimally match a measurement to a baseline. All of the data is then compared with the single baseline to determine the presence of damage. Yet, for these methods to be effective, the measurement and the baseline must satisfy the inherent assumptions of the temperature compensation method. In many scenarios, these assumptions are wrong, the methods generate error, and damage detection fails. To improve damage detection, a multi-measurement damage detection method is introduced. By using each measurement in the dataset as a baseline, error caused by imperfect temperature compensation is reduced. The multi-measurement method increases the detection effectiveness of our damage metric, or damage indicator, over time and reduces the presence of additional peaks caused by temperature that could be mistaken for damage. By using many baselines, the variance of the damage indicator is reduced and the effects from damage are amplified. Notably, the multi-measurement improves damage detection over single-measurement methods. This is demonstrated through an increase in the maximum of our damage signature from 0.55 to 0.95 (where large values, up to a maximum of one, represent a statistically significant change in the data due to damage). |
Joel B Harley Statistical lamb wave localization based on extreme value theory Inproceedings Proc. of the Review of Quantitative Nondestructive Evaluation, pp. 090004, American Institute of Physics, 2018. @inproceedings{Harley2018-fh, title = {Statistical lamb wave localization based on extreme value theory}, author = {Joel B Harley}, doi = {10.1063/1.5031567}, year = {2018}, date = {2018-04-01}, booktitle = {Proc. of the Review of Quantitative Nondestructive Evaluation}, volume = {1949}, pages = {090004}, publisher = {American Institute of Physics}, abstract = {Guided wave localization methods based on delay-and-sum imaging, matched field processing, and other techniques have been designed and researched to create images that locate and describe structural damage. The maximum value of these images typically represent an estimated damage location. Yet, it is often unclear if this maximum value, or any other value in the image, is a statistically significant indicator of damage. Furthermore, there are currently few, if any, approaches to assess the statistical significance of guided wave localization images. As a result, we present statistical delay-and-sum and statistical matched field processing localization methods to create statistically significant images of damage. Our framework uses constant rate of false alarm statistics and extreme value theory to detect damage with little prior information. We demonstrate our methods with in situ guided wave data from an aluminum plate to detect two 0.75?cm diameter holes. Our results show an expected improvement in statistical significance as the number of sensors increase. With seventeen sensors, both methods successfully detect damage with statistical significance.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Guided wave localization methods based on delay-and-sum imaging, matched field processing, and other techniques have been designed and researched to create images that locate and describe structural damage. The maximum value of these images typically represent an estimated damage location. Yet, it is often unclear if this maximum value, or any other value in the image, is a statistically significant indicator of damage. Furthermore, there are currently few, if any, approaches to assess the statistical significance of guided wave localization images. As a result, we present statistical delay-and-sum and statistical matched field processing localization methods to create statistically significant images of damage. Our framework uses constant rate of false alarm statistics and extreme value theory to detect damage with little prior information. We demonstrate our methods with in situ guided wave data from an aluminum plate to detect two 0.75?cm diameter holes. Our results show an expected improvement in statistical significance as the number of sensors increase. With seventeen sensors, both methods successfully detect damage with statistical significance. |
Joseph Melville; Supreet K Alguri; Chris Deemer; Joel B Harley Structural damage detection using deep learning of ultrasonic guided waves Inproceedings Proc. of the Review of Quantitative Nondestructive Evaluation, pp. 230004, American Institute of Physics, 2018. @inproceedings{Melville2018-xt, title = {Structural damage detection using deep learning of ultrasonic guided waves}, author = {Joseph Melville and Supreet K Alguri and Chris Deemer and Joel B Harley}, doi = {10.1063/1.5031651}, year = {2018}, date = {2018-04-01}, booktitle = {Proc. of the Review of Quantitative Nondestructive Evaluation}, volume = {1949}, pages = {230004}, publisher = {American Institute of Physics}, abstract = {Structural health monitoring using ultrasonic guided waves relies on accurate interpretation of guided wave propagation to distinguish damage state indicators. However, traditional physics based models do not provide an accurate representation, and classic data driven techniques, such as a support vector machine, are too simplistic to capture the complex nature of ultrasonic guide waves. To address this challenge, this paper uses a deep learning interpretation of ultrasonic guided waves to achieve fast, accurate, and automated structural damaged detection. To achieve this, full wavefield scans of thin metal plates are used, half from the undamaged state and half from the damaged state. This data is used to train our deep network to predict the damage state of a plate with 99.98% accuracy given signals from just 10 spatial locations on the plate, as compared to that of a support vector machine (SVM), which achieved a 62% accuracy.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Structural health monitoring using ultrasonic guided waves relies on accurate interpretation of guided wave propagation to distinguish damage state indicators. However, traditional physics based models do not provide an accurate representation, and classic data driven techniques, such as a support vector machine, are too simplistic to capture the complex nature of ultrasonic guide waves. To address this challenge, this paper uses a deep learning interpretation of ultrasonic guided waves to achieve fast, accurate, and automated structural damaged detection. To achieve this, full wavefield scans of thin metal plates are used, half from the undamaged state and half from the damaged state. This data is used to train our deep network to predict the damage state of a plate with 99.98% accuracy given signals from just 10 spatial locations on the plate, as compared to that of a support vector machine (SVM), which achieved a 62% accuracy. |
Soroosh Sabeti; Joel B Harley Two-dimensional sparse wavenumber recovery for guided wavefields Inproceedings Proc. of the Review of Quantitative Nondestructive Evaluation, pp. 230003, American Institute of Physics, 2018. @inproceedings{Sabeti2018-np, title = {Two-dimensional sparse wavenumber recovery for guided wavefields}, author = {Soroosh Sabeti and Joel B Harley}, doi = {10.1063/1.5031650}, year = {2018}, date = {2018-04-01}, booktitle = {Proc. of the Review of Quantitative Nondestructive Evaluation}, volume = {1949}, pages = {230003}, publisher = {American Institute of Physics}, abstract = {The multi-modal and dispersive behavior of guided waves is often characterized by their dispersion curves, which describe their frequency-wavenumber behavior. In prior work, compressive sensing based techniques, such as sparse wavenumber analysis (SWA), have been capable of recovering dispersion curves from limited data samples. A major limitation of SWA, however, is the assumption that the structure is isotropic. As a result, SWA fails when applied to composites and other anisotropic structures. There have been efforts to address this issue in the literature, but they either are not easily generalizable or do not sufficiently express the data. In this paper, we enhance the existing approaches by employing a two-dimensional wavenumber model to account for direction-dependent velocities in anisotropic media. We integrate this model with tools from compressive sensing to reconstruct a wavefield from incomplete data. Specifically, we create a modified two-dimensional orthogonal matching pursuit algorithm that takes an undersampled wavefield image, with specified unknown elements, and determines its sparse wavenumber characteristics. We then recover the entire wavefield from the sparse representations obtained with our small number of data samples.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The multi-modal and dispersive behavior of guided waves is often characterized by their dispersion curves, which describe their frequency-wavenumber behavior. In prior work, compressive sensing based techniques, such as sparse wavenumber analysis (SWA), have been capable of recovering dispersion curves from limited data samples. A major limitation of SWA, however, is the assumption that the structure is isotropic. As a result, SWA fails when applied to composites and other anisotropic structures. There have been efforts to address this issue in the literature, but they either are not easily generalizable or do not sufficiently express the data. In this paper, we enhance the existing approaches by employing a two-dimensional wavenumber model to account for direction-dependent velocities in anisotropic media. We integrate this model with tools from compressive sensing to reconstruct a wavefield from incomplete data. Specifically, we create a modified two-dimensional orthogonal matching pursuit algorithm that takes an undersampled wavefield image, with specified unknown elements, and determines its sparse wavenumber characteristics. We then recover the entire wavefield from the sparse representations obtained with our small number of data samples. |
Alexander C Douglass; Joel B Harley Dynamic Time Warping Temperature Compensation for Guided Wave Structural Health Monitoring Journal Article IEEE Trans. Ultrason. Ferroelectr. Freq. Control, PP (99), pp. 1–1, 2018. @article{Douglass2018-vq, title = {Dynamic Time Warping Temperature Compensation for Guided Wave Structural Health Monitoring}, author = {Alexander C Douglass and Joel B Harley}, url = {https://www.researchgate.net/publication/323622965_Dynamic_Time_Warping_Temperature_Compensation_for_Guided_Wave_Structural_Health_Monitoring}, doi = {10.1109/TUFFC.2018.2813278}, year = {2018}, date = {2018-01-01}, journal = {IEEE Trans. Ultrason. Ferroelectr. Freq. Control}, volume = {PP}, number = {99}, pages = {1--1}, abstract = {Guided wave structural health monitoring is widely researched for remotely inspecting large structural areas. To detect, locate, and characterize damage, guided wave methods often compare data to a baseline signal. Yet, environmental variations create large differences between the baseline and the collected measurements. These variations hide damage signatures and cause false detection. Temperature compensation algorithms, such as baseline signal stretch and the scale transform, have been used to optimally realign data to a baseline. While these methods are effective in some conditions, their performance deteriorates in the presence of large temperature variations, long propagation distances, and high frequencies. In this paper, we use dynamic time warping to better align guided wave data and to overcome these errors. When compared with stretch-based methods, we show that dynamic time warping is more robust to the above errors and more accurately detects damage with weak ultrasonic signatures.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Guided wave structural health monitoring is widely researched for remotely inspecting large structural areas. To detect, locate, and characterize damage, guided wave methods often compare data to a baseline signal. Yet, environmental variations create large differences between the baseline and the collected measurements. These variations hide damage signatures and cause false detection. Temperature compensation algorithms, such as baseline signal stretch and the scale transform, have been used to optimally realign data to a baseline. While these methods are effective in some conditions, their performance deteriorates in the presence of large temperature variations, long propagation distances, and high frequencies. In this paper, we use dynamic time warping to better align guided wave data and to overcome these errors. When compared with stretch-based methods, we show that dynamic time warping is more robust to the above errors and more accurately detects damage with weak ultrasonic signatures. |
K Supreet Alguri; Joseph Melville; Joel B Harley Baseline-free guided wave damage detection with surrogate data and dictionary learning Journal Article J. Acoust. Soc. Am., 143 (6), pp. 3807, 2018. @article{Alguri2018-ng, title = {Baseline-free guided wave damage detection with surrogate data and dictionary learning}, author = {K Supreet Alguri and Joseph Melville and Joel B Harley}, doi = {10.1121/1.5042240}, year = {2018}, date = {2018-01-01}, journal = {J. Acoust. Soc. Am.}, volume = {143}, number = {6}, pages = {3807}, abstract = {In guided wave structural health monitoring, damage detection is often accomplished by comparing measurements before damage (i.e., baseline data) and after damage (i.e., test data). Yet, in practical scenarios, baseline data is often unavailable. Data from surrogate structures (structures similar to the test structure) could replace baseline data, but due to small differences in material properties, such as thickness, temperature, and other effects, this data is often unreliable. In this paper, a dictionary learning framework overcomes this challenge and detects damage with surrogate information. The framework combines wave propagation characteristics of a test structure with geometric information from surrogate structures to create a synthetic damage-free baseline. The test data is compared with the synthetic baseline to detect damage. The framework is evaluated with four 108 mm $times$108 mm plates: two 1.6-mm thick aluminum plates, one 1.6-mm thick steel plate, and one 6.25 mm thick aluminum plate. The framework is applied to each test structure after learning from plates with different material properties and thicknesses. With full wavefield data, this paper successfully isolates reflections from a mass without using explicit baseline data. Furthermore, with sparse guided wave data, this paper shows that a drop in a correlation coefficient can detect a mass without using explicit baseline data.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In guided wave structural health monitoring, damage detection is often accomplished by comparing measurements before damage (i.e., baseline data) and after damage (i.e., test data). Yet, in practical scenarios, baseline data is often unavailable. Data from surrogate structures (structures similar to the test structure) could replace baseline data, but due to small differences in material properties, such as thickness, temperature, and other effects, this data is often unreliable. In this paper, a dictionary learning framework overcomes this challenge and detects damage with surrogate information. The framework combines wave propagation characteristics of a test structure with geometric information from surrogate structures to create a synthetic damage-free baseline. The test data is compared with the synthetic baseline to detect damage. The framework is evaluated with four 108 mm $times$108 mm plates: two 1.6-mm thick aluminum plates, one 1.6-mm thick steel plate, and one 6.25 mm thick aluminum plate. The framework is applied to each test structure after learning from plates with different material properties and thicknesses. With full wavefield data, this paper successfully isolates reflections from a mass without using explicit baseline data. Furthermore, with sparse guided wave data, this paper shows that a drop in a correlation coefficient can detect a mass without using explicit baseline data. |
Cynthia Furse; Naveen K T Jayakumar; Evan Benoit; Mashad U Saleh; Josiah LaCombe; Michael Scarpulla; Joel B Harley; Samuel Kingston; Brent Waddoups; Chris Deline Spread Spectrum Time Domain Reflectometry for Complex Impedances: Application to PV Arrays Inproceedings Proc. of IEEE AUTOTESTCON, pp. 1–4, 2018. @inproceedings{Furse2018-fl, title = {Spread Spectrum Time Domain Reflectometry for Complex Impedances: Application to PV Arrays}, author = {Cynthia Furse and Naveen K T Jayakumar and Evan Benoit and Mashad U Saleh and Josiah LaCombe and Michael Scarpulla and Joel B Harley and Samuel Kingston and Brent Waddoups and Chris Deline}, doi = {10.1109/autest.2018.8532521}, year = {2018}, date = {2018-01-01}, booktitle = {Proc. of IEEE AUTOTESTCON}, pages = {1--4}, abstract = {Spread spectrum time domain reflectometry (SSTDR) has previously been used for detection and location of intermittent faults on live electrical wiring. These intermittent faults can be open circuits, short circuits, or resistive changes, all of which preserve the original shape of the SSTDR correlated waveform. But things are very different when SSTDR encounters a complex impedance discontinuity such as a capacitor or inductor. In this case, the reflection is a function of frequency, changing the shape of the SSTDR signature. In this paper, we will show the SSTDR response to single capacitors and inductors. We will also explore how SSTDR responds to arrays of PV panels (which are capacitive) connected by wires. We will show both simulations and measurements. In some configurations, it is relatively easy to see faults, although algorithms are still under development. In other configurations, little change occurs, which makes it very difficult to create a system for testing for these faults.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Spread spectrum time domain reflectometry (SSTDR) has previously been used for detection and location of intermittent faults on live electrical wiring. These intermittent faults can be open circuits, short circuits, or resistive changes, all of which preserve the original shape of the SSTDR correlated waveform. But things are very different when SSTDR encounters a complex impedance discontinuity such as a capacitor or inductor. In this case, the reflection is a function of frequency, changing the shape of the SSTDR signature. In this paper, we will show the SSTDR response to single capacitors and inductors. We will also explore how SSTDR responds to arrays of PV panels (which are capacitive) connected by wires. We will show both simulations and measurements. In some configurations, it is relatively easy to see faults, although algorithms are still under development. In other configurations, little change occurs, which makes it very difficult to create a system for testing for these faults. |
Joel B Harley; Chen Ciang Chia Statistical partial wavefield imaging using Lamb wave signals Journal Article Structural Health Monitoring, 17 (4), pp. 919–935, 2018. @article{Harley2018-mf, title = {Statistical partial wavefield imaging using Lamb wave signals}, author = {Joel B Harley and Chen Ciang Chia}, doi = {10.1177/1475921717727160}, year = {2018}, date = {2018-01-01}, journal = {Structural Health Monitoring}, volume = {17}, number = {4}, pages = {919--935}, publisher = {SAGE Publications}, abstract = {This article presents a baseline-free, model-driven, statistical damage detection and imaging framework for guided waves measured from partial (i.e. non-dense) wavefield scans. Wavefield analysis is an effective non-contact technique for non-destructive evaluation. Yet, there are several limitations to practically implement wavefield methods. These limitations include slow data acquisition and a lack of statistical reliability. Our approach addresses both of these challenges. We use sparse wavenumber analysis, sparse wavenumber synthesis, and data-fitting optimization to accurately model damage-free wavefield data. We then combine this model with matched field processing to image damage from a small number of partial wavefield measurements. We further derive a hypothesis test based on extreme value theory to statistically detect damage. We test our framework with Lamb wave measurements from a steel plate. With 70 experimental wavefield measurements, we achieve an empirical probability of damage detection of more than 98%, an empirical probability of false alarm of less than 0.17%, and an accurate image of the damage.}, keywords = {}, pubstate = {published}, tppubtype = {article} } This article presents a baseline-free, model-driven, statistical damage detection and imaging framework for guided waves measured from partial (i.e. non-dense) wavefield scans. Wavefield analysis is an effective non-contact technique for non-destructive evaluation. Yet, there are several limitations to practically implement wavefield methods. These limitations include slow data acquisition and a lack of statistical reliability. Our approach addresses both of these challenges. We use sparse wavenumber analysis, sparse wavenumber synthesis, and data-fitting optimization to accurately model damage-free wavefield data. We then combine this model with matched field processing to image damage from a small number of partial wavefield measurements. We further derive a hypothesis test based on extreme value theory to statistically detect damage. We test our framework with Lamb wave measurements from a steel plate. With 70 experimental wavefield measurements, we achieve an empirical probability of damage detection of more than 98%, an empirical probability of false alarm of less than 0.17%, and an accurate image of the damage. |
Manish Roy; Kenneth Walton; Joel B Harley; Mikhail Skliar Ultrasonic Evaluation of Segmental Variability in Additively Manufactured Metal Components Inproceedings Proc. of the IEEE International Ultrasonics Symposium, pp. 1–4, 2018. @inproceedings{Roy2018-oh, title = {Ultrasonic Evaluation of Segmental Variability in Additively Manufactured Metal Components}, author = {Manish Roy and Kenneth Walton and Joel B Harley and Mikhail Skliar}, doi = {10.1109/ULTSYM.2018.8579663}, year = {2018}, date = {2018-01-01}, booktitle = {Proc. of the IEEE International Ultrasonics Symposium}, pages = {1--4}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2017 |
Soroosh Sabeti; Joel B Harley Guided wave retrieval from temporally undersampled data Inproceedings Proc. of the IEEE International Ultrasonics Symposium, pp. 1–4, 2017. @inproceedings{Sabeti2017-qj, title = {Guided wave retrieval from temporally undersampled data}, author = {Soroosh Sabeti and Joel B Harley}, url = {https://www.researchgate.net/publication/321236390_Guided_wave_retrieval_from_temporally_undersampled_data}, doi = {10.1109/ULTSYM.2017.8091665}, year = {2017}, date = {2017-09-06}, booktitle = {Proc. of the IEEE International Ultrasonics Symposium}, pages = {1--4}, abstract = {The acquisition of ultrasonic guided waves for full wavefield nondestructive evaluation (NDE) applications is often a time-consuming procedure. Moreover, the amount of data to be stored over time can be enormous. Consequently, to improve storage efficiency and reduce the acquisition time, it is desirable to retrieve information from partially garnered (undersampled) data. Efforts in the literature aimed at addressing this issue exist, mostly through data recovery from spatially undersampled wavefields. In this paper, we present a compressive sensing based methodology to retrieve guided wavefields from data undersampled in the temporal domain. We implement this method by recovering the dispersion curves of guided Lamb waves (i.e., their sparse representation in the frequency-wavenumber domain) from a few temporal measurements. From this representation, we subsequently reconstruct the entire wavefield. We demonstrate a 97 percent reconstruction accuracy (in terms of correlation coefficient) for simulated Lamb waves containing frequencies ranging from 150 kHz to 350 kHz, but generated with an effective sampling rate of only 50 kHz.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The acquisition of ultrasonic guided waves for full wavefield nondestructive evaluation (NDE) applications is often a time-consuming procedure. Moreover, the amount of data to be stored over time can be enormous. Consequently, to improve storage efficiency and reduce the acquisition time, it is desirable to retrieve information from partially garnered (undersampled) data. Efforts in the literature aimed at addressing this issue exist, mostly through data recovery from spatially undersampled wavefields. In this paper, we present a compressive sensing based methodology to retrieve guided wavefields from data undersampled in the temporal domain. We implement this method by recovering the dispersion curves of guided Lamb waves (i.e., their sparse representation in the frequency-wavenumber domain) from a few temporal measurements. From this representation, we subsequently reconstruct the entire wavefield. We demonstrate a 97 percent reconstruction accuracy (in terms of correlation coefficient) for simulated Lamb waves containing frequencies ranging from 150 kHz to 350 kHz, but generated with an effective sampling rate of only 50 kHz. |
K Supreet Alguri; Chen Ciang Chia; Joel B Harley Model-driven, Wavefield Baseline Subtraction for Damage Visualization using Dictionary Learning Journal Article Proc. of the International Workshop on Structural Health Monitoring, 2017. @article{Supreet_Alguri2017-re, title = {Model-driven, Wavefield Baseline Subtraction for Damage Visualization using Dictionary Learning}, author = {K Supreet Alguri and Chen Ciang Chia and Joel B Harley}, url = {Model-driven, Wavefield Baseline Subtraction for Damage Visualization using Dictionary Learning}, doi = {10.12783/shm2017/14120}, year = {2017}, date = {2017-09-01}, journal = {Proc. of the International Workshop on Structural Health Monitoring}, abstract = {Wavefield acquisition is a powerful visualization tool for studying wave propagation. Yet, without a baseline, damage visualization is a challenge because the damage wavefield is usually orders of magnitude weaker than the incident waves. Researchers have created several baseline-free approaches for suppressing incident waves, but these methods often rely on simple assumptions. We address this challenge with a dictionary learning framework that uses simulation data to guide the suppression of incident waves. Dictionary learning produces new baselines with geometric features similar to the simulations as well as velocity and frequency response features similar to the experimental data. We show that our framework can visualize reflections from a circular 2 mm diameter half thickness hole in a 10 cm $times$ 10 cm steel plate without a baseline.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Wavefield acquisition is a powerful visualization tool for studying wave propagation. Yet, without a baseline, damage visualization is a challenge because the damage wavefield is usually orders of magnitude weaker than the incident waves. Researchers have created several baseline-free approaches for suppressing incident waves, but these methods often rely on simple assumptions. We address this challenge with a dictionary learning framework that uses simulation data to guide the suppression of incident waves. Dictionary learning produces new baselines with geometric features similar to the simulations as well as velocity and frequency response features similar to the experimental data. We show that our framework can visualize reflections from a circular 2 mm diameter half thickness hole in a 10 cm $times$ 10 cm steel plate without a baseline. |
Alexander Douglass; Joel B Harley Dynamic time warping for temperature compensation in structural health monitoring Inproceedings Proc. of the Review of Quantitative Nondestructive Evaluation, pp. 020017, 2017. @inproceedings{Douglass2017-fe, title = {Dynamic time warping for temperature compensation in structural health monitoring}, author = {Alexander Douglass and Joel B Harley}, url = {https://www.researchgate.net/publication/313803227_Dynamic_time_warping_for_temperature_compensation_in_structural_health_monitoring}, doi = {10.1063/1.4974558}, year = {2017}, date = {2017-02-16}, booktitle = {Proc. of the Review of Quantitative Nondestructive Evaluation}, journal = {AIP Conf. Proc.}, volume = {1806}, number = {1}, pages = {020017}, abstract = {Guided wave structural health monitoring uses ultrasonic waves to identify changes in structures. To identify these changes, most guided wave methods require a pristine baseline measurement with which other measurements are compared. Damage signatures arise when there is a deviation between the baseline and the recorded measurement. However, temperature significantly complicates this analysis by creating misalignment between the baseline and measurements. This leads to false alarms of damage and significantly reduces the reliability of these systems. Several methods have been created to account for these temperature perturbations. Yet, most of these compensation methods fail in harsh, highly variable temperature conditions or require a prohibitive amount of prior data. In this paper, we use an algorithm known as dynamic time warping to compensate for temperature in these harsh conditions. We demonstrate that dynamic time warping is able to account for temperature variations whereas the more traditional baseline signal stretch method is unable to resolve damage under high temperature fluctuations.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Guided wave structural health monitoring uses ultrasonic waves to identify changes in structures. To identify these changes, most guided wave methods require a pristine baseline measurement with which other measurements are compared. Damage signatures arise when there is a deviation between the baseline and the recorded measurement. However, temperature significantly complicates this analysis by creating misalignment between the baseline and measurements. This leads to false alarms of damage and significantly reduces the reliability of these systems. Several methods have been created to account for these temperature perturbations. Yet, most of these compensation methods fail in harsh, highly variable temperature conditions or require a prohibitive amount of prior data. In this paper, we use an algorithm known as dynamic time warping to compensate for temperature in these harsh conditions. We demonstrate that dynamic time warping is able to account for temperature variations whereas the more traditional baseline signal stretch method is unable to resolve damage under high temperature fluctuations. |
Wenbo Zhao; Ming Li; Joel B Harley; Yuanwei Jin; José M F Moura; Jimmy Zhu Reconstruction of Lamb wave dispersion curves by sparse representation with continuity constraints Journal Article J. Acoust. Soc. Am., 141 (2), pp. 749, 2017. @article{Zhao2017-zd, title = {Reconstruction of Lamb wave dispersion curves by sparse representation with continuity constraints}, author = {Wenbo Zhao and Ming Li and Joel B Harley and Yuanwei Jin and José M F Moura and Jimmy Zhu}, url = {https://www.researchgate.net/publication/313323591_Reconstruction_of_Lamb_wave_dispersion_curves_by_sparse_representation_with_continuity_constraints}, doi = {10.1121/1.4974063}, year = {2017}, date = {2017-02-01}, journal = {J. Acoust. Soc. Am.}, volume = {141}, number = {2}, pages = {749}, abstract = {Ultrasonic Lamb waves are a widely used research tool for nondestructive structural health monitoring. They travel long distances with little attenuation, enabling the interrogation of large areas. To analyze Lamb wave propagation data, it is often important to know precisely how they propagate. Yet, since wave propagation is influenced by many factors, including material properties, temperature, and other varying conditions, acquiring this knowledge is a significant challenge. In prior work, this information has been recovered by reconstructing Lamb wave dispersion curves with sparse wavenumber analysis. While effective, sparse wavenumber analysis requires a large number of sensors and is sensitive to noise in the data. In this paper, it extended and significantly improved by constraining the reconstructed dispersion curves to be continuous across frequencies. To enforce this constraint, it is included explicitly in a sparse optimization formulation, and by including in the reconstruction an edge detection step to remove outliers, and by using variational Bayesian Gaussian mixture models to predict missing values. The method is validated with simulation and experimental data. Significant improved performance is demonstrated over the original sparse wavenumber analysis approach in reconstructing the dispersion curves, in synthesizing noise-removed signals, in reducing the number of measurements, and in localizing damage.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Ultrasonic Lamb waves are a widely used research tool for nondestructive structural health monitoring. They travel long distances with little attenuation, enabling the interrogation of large areas. To analyze Lamb wave propagation data, it is often important to know precisely how they propagate. Yet, since wave propagation is influenced by many factors, including material properties, temperature, and other varying conditions, acquiring this knowledge is a significant challenge. In prior work, this information has been recovered by reconstructing Lamb wave dispersion curves with sparse wavenumber analysis. While effective, sparse wavenumber analysis requires a large number of sensors and is sensitive to noise in the data. In this paper, it extended and significantly improved by constraining the reconstructed dispersion curves to be continuous across frequencies. To enforce this constraint, it is included explicitly in a sparse optimization formulation, and by including in the reconstruction an edge detection step to remove outliers, and by using variational Bayesian Gaussian mixture models to predict missing values. The method is validated with simulation and experimental data. Significant improved performance is demonstrated over the original sparse wavenumber analysis approach in reconstructing the dispersion curves, in synthesizing noise-removed signals, in reducing the number of measurements, and in localizing damage. |
David W Greve; Jaime Parra; Mario Bergés; Joel B Harley; Warren R Junker; Irving J Oppenheim; Zitian Zhang An effect at the source creates ringing in a thick plate Inproceedings Proc. of the IEEE International Ultrasonics Symposium, pp. 1–4, 2017. @inproceedings{Greve2017-nb, title = {An effect at the source creates ringing in a thick plate}, author = {David W Greve and Jaime Parra and Mario Bergés and Joel B Harley and Warren R Junker and Irving J Oppenheim and Zitian Zhang}, url = {https://www.researchgate.net/publication/321236720_An_effect_at_the_source_creates_ringing_in_a_thick_plate}, doi = {10.1109/ULTSYM.2017.8092662}, year = {2017}, date = {2017-01-01}, booktitle = {Proc. of the IEEE International Ultrasonics Symposium}, pages = {1--4}, abstract = {We explore the frequency dependence of piezoceramic disc transducers bonded to plates. For some transducer dimensions, the frequency response shows a sharp peak. This behavior is inconsistent with the use of short pulses or broadband chirps for flaw localization. We use finite element simulation to show that the highly resonant behavior is caused by a high-strain region immediately beneath the transducer. We show that the transducer dimensions can be altered to acheive larger transducer bandwidth.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We explore the frequency dependence of piezoceramic disc transducers bonded to plates. For some transducer dimensions, the frequency response shows a sharp peak. This behavior is inconsistent with the use of short pulses or broadband chirps for flaw localization. We use finite element simulation to show that the highly resonant behavior is caused by a high-strain region immediately beneath the transducer. We show that the transducer dimensions can be altered to acheive larger transducer bandwidth. |
K Supreet Alguri; Jennifer E Michaels; Joel B Harley Robust baseline subtraction for ultrasonic full wavefield analysis Inproceedings Proc. of the Review of Quantitative Nondestructive Evaluation, pp. 020005, 2017. @inproceedings{Alguri2017-ttb, title = {Robust baseline subtraction for ultrasonic full wavefield analysis}, author = {K Supreet Alguri and Jennifer E Michaels and Joel B Harley}, url = {https://www.researchgate.net/publication/309852550_Robust_Baseline_Subtraction_for_Ultrasonic_Full_Wavefield_Analysis}, doi = {10.1063/1.4974546}, year = {2017}, date = {2017-01-01}, booktitle = {Proc. of the Review of Quantitative Nondestructive Evaluation}, volume = {1806}, number = {1}, pages = {020005}, abstract = {Full wavefield analysis is used to study and characterize the interaction between waves and structural damage. Yet, as wavefields are measured and as damage evolves in a structure, environmental and operational variations can significantly affect wave propagation. Several approaches, including time-stretching and optimal baseline selection methods, can reduce variations, but these methods are often limited to specific effects, are ineffective for large environmental variations, or require an impractical number of prior baseline measurements. This paper presents a robust methodology for subtracting wavefields and isolating wave-damage interactions. The method is based on dictionary learning. It is robust to multiple types of environmental and operational variations and requires only one initial baseline. We learn the dictionary, which describes wave propagation for a particular wavefield, based on multiple frequencies of a baseline wavefield. We then use the dictionary and sparse regression to create new baselines for measurements with different environmental and operational conditions. The new baseline is then subtracted from the new wavefield to isolate damage wavefield.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Full wavefield analysis is used to study and characterize the interaction between waves and structural damage. Yet, as wavefields are measured and as damage evolves in a structure, environmental and operational variations can significantly affect wave propagation. Several approaches, including time-stretching and optimal baseline selection methods, can reduce variations, but these methods are often limited to specific effects, are ineffective for large environmental variations, or require an impractical number of prior baseline measurements. This paper presents a robust methodology for subtracting wavefields and isolating wave-damage interactions. The method is based on dictionary learning. It is robust to multiple types of environmental and operational variations and requires only one initial baseline. We learn the dictionary, which describes wave propagation for a particular wavefield, based on multiple frequencies of a baseline wavefield. We then use the dictionary and sparse regression to create new baselines for measurements with different environmental and operational conditions. The new baseline is then subtracted from the new wavefield to isolate damage wavefield. |
2016 |
Spencer Shiveley; Alexander Douglass; Benjamin Posch; Joel B Harley Guided wave structural health monitoring with large data sets Inproceedings Proc. of the IEEE International Ultrasonics Symposium, pp. 1–4, 2016. @inproceedings{Shiveley2016-tp, title = {Guided wave structural health monitoring with large data sets}, author = {Spencer Shiveley and Alexander Douglass and Benjamin Posch and Joel B Harley}, url = {https://www.researchgate.net/publication/309777692_Guided_wave_structural_health_monitoring_with_large_data_sets}, doi = {10.1109/ULTSYM.2016.7728712}, year = {2016}, date = {2016-09-01}, booktitle = {Proc. of the IEEE International Ultrasonics Symposium}, pages = {1--4}, abstract = {Structural health monitoring systems collect and process large collections of data taken over many years of a structure's service. Ultrasonic guided wave systems, in particular, must process an abundance of time-domain waveform data from widely distributed sensors. As few as 8 sensors that transmit and receive ultrasonic waves in pitch-catch mode every 10 minutes can accumulate over one terabyte of data in five to ten years. This number quickly rises as systems grow in size and complexity. As a result, computation and storage efficiency is extremely important, and current guided wave damage detection technologies cannot efficiently process such large data sets. This paper presents an approach based on random projection theory, which scales well with large data sets, to address this challenge. With 1447 experimental guided wave measurements, we demonstrate our algorithms to achieve a 2537 times improvement in computational speed with less than a 3% reduction in accuracy.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Structural health monitoring systems collect and process large collections of data taken over many years of a structure's service. Ultrasonic guided wave systems, in particular, must process an abundance of time-domain waveform data from widely distributed sensors. As few as 8 sensors that transmit and receive ultrasonic waves in pitch-catch mode every 10 minutes can accumulate over one terabyte of data in five to ten years. This number quickly rises as systems grow in size and complexity. As a result, computation and storage efficiency is extremely important, and current guided wave damage detection technologies cannot efficiently process such large data sets. This paper presents an approach based on random projection theory, which scales well with large data sets, to address this challenge. With 1447 experimental guided wave measurements, we demonstrate our algorithms to achieve a 2537 times improvement in computational speed with less than a 3% reduction in accuracy. |
Joel B Harley Predictive Guided Wave Models Through Sparse Modal Representations Journal Article Proc. IEEE, 104 (8), pp. 1604–1619, 2016. @article{Harley2016-bt, title = {Predictive Guided Wave Models Through Sparse Modal Representations}, author = {Joel B Harley}, url = {https://www.researchgate.net/publication/286650088_Predictive_Guided_Wave_Models_Through_Sparse_Modal_Representations}, doi = {10.1109/JPROC.2015.2481438}, year = {2016}, date = {2016-08-01}, journal = {Proc. IEEE}, volume = {104}, number = {8}, pages = {1604--1619}, abstract = {Ultrasonic guided waves are an attractive tool for structural health monitoring due to their capability to rapidly assess large regions of a structure. Yet, most guided wave based methods for detecting, locating, and classifying structural damage rely on our ability to accurately predict guided wave behavior. Characterizing and predicting guided wave behavior is difficult, particularly in mechanically complex materials such as fiber-reinforced composites. In this paper, we address this challenge through a sparse wavenumber analysis framework. Sparse wavenumber analysis integrates physics-based models, signal processing algorithms for compressive sensing, and a small number of local measurements to predict global wave behavior. We implement sparse wavenumber analysis for three wave systems: standing waves on a string, Lamb waves in an isotropic plate, and guided waves in a unidirectional, anisotropic plate. Through the use of simulation and experimental data, we show that sparse wavenumber analysis can accurately recover the sparse representations (i.e., the eigenmodes) of each system and then use these representations to predict global wave behavior. For the anisotropic plate, we accurately predict 149765 experimental time-domain measurements from only 36 local measurements.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Ultrasonic guided waves are an attractive tool for structural health monitoring due to their capability to rapidly assess large regions of a structure. Yet, most guided wave based methods for detecting, locating, and classifying structural damage rely on our ability to accurately predict guided wave behavior. Characterizing and predicting guided wave behavior is difficult, particularly in mechanically complex materials such as fiber-reinforced composites. In this paper, we address this challenge through a sparse wavenumber analysis framework. Sparse wavenumber analysis integrates physics-based models, signal processing algorithms for compressive sensing, and a small number of local measurements to predict global wave behavior. We implement sparse wavenumber analysis for three wave systems: standing waves on a string, Lamb waves in an isotropic plate, and guided waves in a unidirectional, anisotropic plate. Through the use of simulation and experimental data, we show that sparse wavenumber analysis can accurately recover the sparse representations (i.e., the eigenmodes) of each system and then use these representations to predict global wave behavior. For the anisotropic plate, we accurately predict 149765 experimental time-domain measurements from only 36 local measurements. |
Alexander C Douglass; Joel B Harley Dynamic time warping: Compensating for temperature variations Presentation 01.04.2016, (Meeting of the Acoustical Society of America). @misc{Douglass2016-xs, title = {Dynamic time warping: Compensating for temperature variations}, author = {Alexander C Douglass and Joel B Harley}, url = {Dynamic time warping: Compensating for temperature variations}, doi = {10.1121/1.4950271}, year = {2016}, date = {2016-04-01}, publisher = {Acoustical Society of America}, series = {Meeting of the Acoustical Society of America}, abstract = {Guided wave structural health monitoring has the potential to monitor large structural areas. Yet, temperature variations are known to misalign measurement data and cause false alarms. Current popular temperature compensation methods include the optimal signal stretch method and interpolation method. While these methods perform well for small temperature changes, they fail in large temperature variations. In this paper, dynamic time warping is used to realign signal responses that have been distorted by temperature. Baseline subtraction between the realigned signal and the baseline can then be used to detect and identify damage responses. We demonstrate dynamic time warping with guided wave measurements taken from an aluminum plate that is heated in cycles from roughly 75°F to 125°F. Dynamic time warping achieves a correlation between the chosen baseline and the pre-damage measurements greater than 90%. Applying no temperature compensation or more traditional methods, such as optimal signal stretch, demonstrate correlations of 0% and 30%, respectively. This shows that dynamic time warping removes temperature effects more accurately than other current approaches.}, note = {Meeting of the Acoustical Society of America}, keywords = {}, pubstate = {published}, tppubtype = {presentation} } Guided wave structural health monitoring has the potential to monitor large structural areas. Yet, temperature variations are known to misalign measurement data and cause false alarms. Current popular temperature compensation methods include the optimal signal stretch method and interpolation method. While these methods perform well for small temperature changes, they fail in large temperature variations. In this paper, dynamic time warping is used to realign signal responses that have been distorted by temperature. Baseline subtraction between the realigned signal and the baseline can then be used to detect and identify damage responses. We demonstrate dynamic time warping with guided wave measurements taken from an aluminum plate that is heated in cycles from roughly 75°F to 125°F. Dynamic time warping achieves a correlation between the chosen baseline and the pre-damage measurements greater than 90%. Applying no temperature compensation or more traditional methods, such as optimal signal stretch, demonstrate correlations of 0% and 30%, respectively. This shows that dynamic time warping removes temperature effects more accurately than other current approaches. |
Peng Gong; Joel B Harley; Mario Berges; Warren R Junker; David W Greve; Irving J Oppenheim Ultrasonic guided wave detection of scatterers on large clad steel plates Inproceedings Proc. of the SPIE Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, pp. 98033O, Proc. of the SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2016. @inproceedings{Gong2016-ov, title = {Ultrasonic guided wave detection of scatterers on large clad steel plates}, author = {Peng Gong and Joel B Harley and Mario Berges and Warren R Junker and David W Greve and Irving J Oppenheim}, url = {https://www.researchgate.net/publication/301575136_Ultrasonic_guided_wave_detection_of_scatterers_on_large_clad_steel_plates}, doi = {10.1117/12.2214393}, year = {2016}, date = {2016-04-01}, booktitle = {Proc. of the SPIE Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems}, volume = {9803}, pages = {98033O}, publisher = {Proc. of the SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring}, abstract = {``Clad steel'' refers to a thick carbon steel structural plate bonded to a corrosion resistant alloy (CRA) plate, such as stainless steel or titanium, and is widely used in industry to construct pressure vessels. The CRA resists the chemically aggressive environment on the interior, but cannot prevent the development of corrosion losses and cracks that limit the continued safe operation of such vessels. At present there are no practical methods to detect such defects from the exposed outer surface of the thick carbon steel plate, often necessitating removing such vessels from service and inspecting them visually from the interior. In previous research, sponsored by industry to detect and localize damage in pressurized piping systems under operational and environmental changes, we investigated a number of data-driven signal processing methods to extract damage information from ultrasonic guided wave pitch-catch records. We now apply those methods to relatively large clad steel plate specimens. We study a sparse array of wafer-type ultrasonic transducers adhered to the carbon steel surface, attempting to localize mass scatterers grease-coupled to the stainless steel surface. We discuss conditions under which localization is achieved by relatively simple first-arrival methods, and other conditions for which data-driven methods are needed; we also discuss observations of plate-like mode properties implied by these results.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } ``Clad steel'' refers to a thick carbon steel structural plate bonded to a corrosion resistant alloy (CRA) plate, such as stainless steel or titanium, and is widely used in industry to construct pressure vessels. The CRA resists the chemically aggressive environment on the interior, but cannot prevent the development of corrosion losses and cracks that limit the continued safe operation of such vessels. At present there are no practical methods to detect such defects from the exposed outer surface of the thick carbon steel plate, often necessitating removing such vessels from service and inspecting them visually from the interior. In previous research, sponsored by industry to detect and localize damage in pressurized piping systems under operational and environmental changes, we investigated a number of data-driven signal processing methods to extract damage information from ultrasonic guided wave pitch-catch records. We now apply those methods to relatively large clad steel plate specimens. We study a sparse array of wafer-type ultrasonic transducers adhered to the carbon steel surface, attempting to localize mass scatterers grease-coupled to the stainless steel surface. We discuss conditions under which localization is achieved by relatively simple first-arrival methods, and other conditions for which data-driven methods are needed; we also discuss observations of plate-like mode properties implied by these results. |
Joel B Harley; Luca De Marchi Multidimensional guided wave dispersion recovery for locating defects in composite materials Inproceedings Proc. of the Review of Quantitative Nondestructive Evaluation, pp. 030009, AIP Publishing, 2016. @inproceedings{Harley2016-ns, title = {Multidimensional guided wave dispersion recovery for locating defects in composite materials}, author = {Joel B Harley and Luca De Marchi}, url = {https://www.researchgate.net/publication/299391586_Multidimensional_Guided_Wave_Dispersion_Recovery_for_Locating_Defects_in_Composite_Materials}, doi = {10.1063/1.4940481}, year = {2016}, date = {2016-02-01}, booktitle = {Proc. of the Review of Quantitative Nondestructive Evaluation}, volume = {1706}, pages = {030009}, publisher = {AIP Publishing}, abstract = {This paper provides a framework for characterizing anisotropic guided waves to locate damage in composite structures. Composite guided wave structural health monitoring is a significant challenge due to anisotropy. Wave velocities and attenuation vary as a function of propagation direction. Traditional localization algorithms, such as triangulation and delay-and-sum beamforming, fail for composite monitoring because they rely on isotropic velocity assumptions. Estimating the anisotropic velocities is also challenging because the inverse problem is inherently ill-posed. We cannot solve for an infinite number of directions with a finite number of measurements. This paper addresses these challenges by deriving a physics-based model for unidirectional anisotropy and integrating it with sparse recovery tools and matched field processing to characterize composite guided waves and locate an acoustic source. We validate our approach with experimental laser doppler vibrometry measurements from a glass fiber reinforced composite panel. We achieve localization accuracies of more than 290 and 49 times better, respectively, than delay-and-sum and matched field processing with isotropic assumptions.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper provides a framework for characterizing anisotropic guided waves to locate damage in composite structures. Composite guided wave structural health monitoring is a significant challenge due to anisotropy. Wave velocities and attenuation vary as a function of propagation direction. Traditional localization algorithms, such as triangulation and delay-and-sum beamforming, fail for composite monitoring because they rely on isotropic velocity assumptions. Estimating the anisotropic velocities is also challenging because the inverse problem is inherently ill-posed. We cannot solve for an infinite number of directions with a finite number of measurements. This paper addresses these challenges by deriving a physics-based model for unidirectional anisotropy and integrating it with sparse recovery tools and matched field processing to characterize composite guided waves and locate an acoustic source. We validate our approach with experimental laser doppler vibrometry measurements from a glass fiber reinforced composite panel. We achieve localization accuracies of more than 290 and 49 times better, respectively, than delay-and-sum and matched field processing with isotropic assumptions. |
K Supreet Alguri; Joel B Harley Consolidating guided wave simulations and experimental data: a dictionary learning approach Inproceedings Proc. of the SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, pp. 98050Y–98050Y–10, International Society for Optics and Photonics, 2016. @inproceedings{Supreet_Alguri2016-pq, title = {Consolidating guided wave simulations and experimental data: a dictionary learning approach}, author = {K Supreet Alguri and Joel B Harley}, url = {https://www.researchgate.net/publication/299834632_Consolidating_Guided_Wave_Simulations_and_Experimental_Data_A_Dictionary_Learning_Approach}, doi = {10.1117/12.2219420}, year = {2016}, date = {2016-01-01}, booktitle = {Proc. of the SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring}, pages = {98050Y--98050Y--10}, publisher = {International Society for Optics and Photonics}, abstract = {Modeling and simulating guided wave propagation in complex, geometric structures is a topic of significant interest in structural health monitoring. These models have the potential to benefit damage detection, localization, and characterization in structures where traditional algorithms fail. Numerical modelling (for example, using finite element or semi-analytical finite element methods) is a popular approach for simulating complex wave behavior. Yet, using these models to improve experimental data analysis remains difficult. Numerical simulations and experimental data rarely match due to uncertainty in the properties of the structures and the guided waves traveling within them. As a result, there is a significant need to reduce this uncertainty by incorporating experimental data into the models. In this paper, we present a dictionary learning framework to address this challenge. Specifically, use dictionary learning to combine numerical wavefield simulations with 24 simulated guided wave measurements with different frequency-dependent velocity characteristics (emulating an experimental system) to make accurate, global predictions about experimental wave behavior. From just 24 measurements, we show that we can predict and extrapolate guided wave behavior with accuracies greater than 92%.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Modeling and simulating guided wave propagation in complex, geometric structures is a topic of significant interest in structural health monitoring. These models have the potential to benefit damage detection, localization, and characterization in structures where traditional algorithms fail. Numerical modelling (for example, using finite element or semi-analytical finite element methods) is a popular approach for simulating complex wave behavior. Yet, using these models to improve experimental data analysis remains difficult. Numerical simulations and experimental data rarely match due to uncertainty in the properties of the structures and the guided waves traveling within them. As a result, there is a significant need to reduce this uncertainty by incorporating experimental data into the models. In this paper, we present a dictionary learning framework to address this challenge. Specifically, use dictionary learning to combine numerical wavefield simulations with 24 simulated guided wave measurements with different frequency-dependent velocity characteristics (emulating an experimental system) to make accurate, global predictions about experimental wave behavior. From just 24 measurements, we show that we can predict and extrapolate guided wave behavior with accuracies greater than 92%. |
2015 |
Sungwon Kim; Bibhisha Uprety; Daniel O Adams; V John Mathews; Joel B Harley Acoustic emission based damage characterization in composite plates using low-velocity impact testing Inproceedings Proc. of the International Workshop on Structural Health Monitoring, pp. 1477–1484, 2015, ISBN: 9781605951119. @inproceedings{21eba826a8c544999397ea65bd181733, title = {Acoustic emission based damage characterization in composite plates using low-velocity impact testing}, author = {Sungwon Kim and Bibhisha Uprety and Daniel O Adams and V John Mathews and Joel B Harley}, url = {https://www.researchgate.net/publication/299391602_Acoustic_Emission_Based_Damage_Characterization_in_Composite_Plates_Using_Low-velocity_Impact_Testing}, doi = {10.12783/SHM2015/185}, isbn = {9781605951119}, year = {2015}, date = {2015-09-15}, booktitle = {Proc. of the International Workshop on Structural Health Monitoring}, volume = {2}, pages = {1477--1484}, abstract = {Acoustic Emission (AE) based Structural Health Monitoring (SHM) systems are important for impact damage detection and characterization in composite structures. Low velocity impact events can induce internal damage without producing visual indications, compromising the materials structural performance while being very difficult to detect and assess by traditional visual inspection method. Over the years, AE sensor networks have been developed to provide real-time monitoring and detect impact events from low velocity impacts over a large area with minimal intrusion to the composite structure. Yet, most AE methods have focused on detecting and locating damage, not characterization and assessment. This paper develops a preliminary framework for characterization of damage resulting from impacts based on AE signal features. Specialized drop-weight impact experiments were designed to study two particular damage states: delamination with minimal fiber damage and fiber-breakage with minimal delamination. Impacted test panels were inspected using ultrasonic Cscans and recorded waveform signals using piezoelectric sensors were characterized to analyze damage response in the time and frequency domains with assistant parameters such as root mean square (RMS) of the waveform}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Acoustic Emission (AE) based Structural Health Monitoring (SHM) systems are important for impact damage detection and characterization in composite structures. Low velocity impact events can induce internal damage without producing visual indications, compromising the materials structural performance while being very difficult to detect and assess by traditional visual inspection method. Over the years, AE sensor networks have been developed to provide real-time monitoring and detect impact events from low velocity impacts over a large area with minimal intrusion to the composite structure. Yet, most AE methods have focused on detecting and locating damage, not characterization and assessment. This paper develops a preliminary framework for characterization of damage resulting from impacts based on AE signal features. Specialized drop-weight impact experiments were designed to study two particular damage states: delamination with minimal fiber damage and fiber-breakage with minimal delamination. Impacted test panels were inspected using ultrasonic Cscans and recorded waveform signals using piezoelectric sensors were characterized to analyze damage response in the time and frequency domains with assistant parameters such as root mean square (RMS) of the waveform |
Ahmad B Zoubi; V J Mathews; Joel B Harley; Daniel O Adams Lamb Waves Mode Decomposition Using the Cross-wigner-ville Distribution Inproceedings Proc. of the International Workshop on Structural Health Monitoring, 2015. @inproceedings{Zoubi2015-pe, title = {Lamb Waves Mode Decomposition Using the Cross-wigner-ville Distribution}, author = {Ahmad B Zoubi and V J Mathews and Joel B Harley and Daniel O Adams}, url = {https://www.researchgate.net/publication/299391830_Lamb_Waves_Mode_Decomposition_Using_the_Cross-wigner-ville_Distribution}, doi = {10.12783/SHM2015/237}, year = {2015}, date = {2015-09-01}, booktitle = {Proc. of the International Workshop on Structural Health Monitoring}, abstract = {Guided Lamb waves have been widely studied for characterizing damage in structures. Lamb waves are characterized by their multimodal and dispersive propagation, which often complicate analysis. As a result, separating the mode components arriving at each acoustic emission sensor is a critical part of many guided wave Structural Health Monitoring (SHM) systems. This paper considers an active SHM system in which the monitored structure is excited with a linear chirp signal using piezoelectric actuators. The measured signals are analyzed to decompose the individual Lamb wave modes. The method employs the cross-Wigner-Ville Distribution (xWVD) between the excitation signal and the received sensor signal and assumes that overlapped modes in the time domain may be separable in the timefrequency domain to reconstruct the modes separately. The mode decomposition method uses a ridge extraction algorithm to identify the location of the individual modes in the time-frequency distribution and separate them using a rectangular window. Once the individual modes are separated in the time-frequency domain, the inverse xWVD is used to reconstruct the modes in the time domain. The method’s effectiveness to separate and reconstruct the first two fundamental Lamb wave modes (zeroth symmetric and zeroth anti-symmetric) is demonstrated in the paper through numerical simulations and experimental results on an aluminum plate. doi: 10.12783/SHM2015/237}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Guided Lamb waves have been widely studied for characterizing damage in structures. Lamb waves are characterized by their multimodal and dispersive propagation, which often complicate analysis. As a result, separating the mode components arriving at each acoustic emission sensor is a critical part of many guided wave Structural Health Monitoring (SHM) systems. This paper considers an active SHM system in which the monitored structure is excited with a linear chirp signal using piezoelectric actuators. The measured signals are analyzed to decompose the individual Lamb wave modes. The method employs the cross-Wigner-Ville Distribution (xWVD) between the excitation signal and the received sensor signal and assumes that overlapped modes in the time domain may be separable in the timefrequency domain to reconstruct the modes separately. The mode decomposition method uses a ridge extraction algorithm to identify the location of the individual modes in the time-frequency distribution and separate them using a rectangular window. Once the individual modes are separated in the time-frequency domain, the inverse xWVD is used to reconstruct the modes in the time domain. The method’s effectiveness to separate and reconstruct the first two fundamental Lamb wave modes (zeroth symmetric and zeroth anti-symmetric) is demonstrated in the paper through numerical simulations and experimental results on an aluminum plate. doi: 10.12783/SHM2015/237 |
Joel B Harley; Jose M F Moura Data-driven and calibration-free lamb wave source localization with sparse sensor arrays Journal Article IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 62 (8), pp. 1516–1529, 2015. @article{Harley2015-je, title = {Data-driven and calibration-free lamb wave source localization with sparse sensor arrays}, author = {Joel B Harley and Jose M F Moura}, url = {https://www.researchgate.net/publication/280970477_Data-Driven_and_Calibration-Free_Lamb_Wave_Source_Localization_With_Sparse_Sensor_Arrays}, doi = {10.1109/TUFFC.2014.006860}, year = {2015}, date = {2015-08-01}, journal = {IEEE Trans. Ultrason. Ferroelectr. Freq. Control}, volume = {62}, number = {8}, pages = {1516--1529}, abstract = {Most Lamb wave localization techniques require that we know the wave's velocity characteristics; yet, in many practical scenarios, velocity estimates can be challenging to acquire, are unavailable, or are unreliable because of the complexity of Lamb waves. As a result, there is a significant need for new methods that can reduce a system's reliance on a priori velocity information. This paper addresses this challenge through two novel source localization methods designed for sparse sensor arrays in isotropic media. Both methods exploit the fundamental sparse structure of a Lamb wave's frequencywavenumber representation. The first method uses sparse recovery techniques to extract velocities from calibration data. The second method uses kurtosis and the support earth mover's distance to measure the sparseness of a Lamb wave's approximate frequency-wavenumber representation. These measures are then used to locate acoustic sources with no prior calibration data. We experimentally study each method with a collection of acoustic emission data measured from a 1.22 m by 1.22 m isotropic aluminum plate. We show that both methods can achieve less than 1 cm localization error and have less systematic error than traditional time-of-arrival localization methods.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Most Lamb wave localization techniques require that we know the wave's velocity characteristics; yet, in many practical scenarios, velocity estimates can be challenging to acquire, are unavailable, or are unreliable because of the complexity of Lamb waves. As a result, there is a significant need for new methods that can reduce a system's reliance on a priori velocity information. This paper addresses this challenge through two novel source localization methods designed for sparse sensor arrays in isotropic media. Both methods exploit the fundamental sparse structure of a Lamb wave's frequencywavenumber representation. The first method uses sparse recovery techniques to extract velocities from calibration data. The second method uses kurtosis and the support earth mover's distance to measure the sparseness of a Lamb wave's approximate frequency-wavenumber representation. These measures are then used to locate acoustic sources with no prior calibration data. We experimentally study each method with a collection of acoustic emission data measured from a 1.22 m by 1.22 m isotropic aluminum plate. We show that both methods can achieve less than 1 cm localization error and have less systematic error than traditional time-of-arrival localization methods. |
Chang Liu; Joel B Harley; Mario Berges; David W Greve; Irving J Oppenheim Robust ultrasonic damage detection under complex environmental conditions using singular value decomposition Journal Article Ultrasonics, 58 , pp. 75–86, 2015. @article{Liu2015-zd, title = {Robust ultrasonic damage detection under complex environmental conditions using singular value decomposition}, author = {Chang Liu and Joel B Harley and Mario Berges and David W Greve and Irving J Oppenheim}, url = {https://www.researchgate.net/publication/271219627_Robust_Ultrasonic_Damage_Detection_under_Complex_Environmental_Conditions_Using_Singular_Value_Decomposition}, doi = {10.1016/j.ultras.2014.12.005}, year = {2015}, date = {2015-04-01}, journal = {Ultrasonics}, volume = {58}, pages = {75--86}, publisher = {Elsevier}, abstract = {Guided wave ultrasonics is an attractive monitoring technique for damage diagnosis in large-scale plate and pipe structures. Damage can be detected by comparing incoming records with baseline records collected on intact structure. However, during long-term monitoring, environmental and operational conditions often vary significantly and produce large changes in the ultrasonic signals, thereby challenging the baseline comparison based damage detection. Researchers developed temperature compensation methods to eliminate the effects of temperature variation, but they have limitations in practical implementations. In this paper, we develop a robust damage detection method based on singular value decomposition (SVD). We show that the orthogonality of singular vectors ensures that the effect of damage and that of environmental and operational variations are separated into different singular vectors. We report on our field ultrasonic monitoring of a 273.05 mm outer diameter pipe segment, which belongs to a hot water piping system in continuous operation. We demonstrate the efficacy of our method on experimental pitch--catch records collected during seven months. We show that our method accurately detects the presence of a mass scatterer, and is robust to the environmental and operational variations exhibited in the practical system.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Guided wave ultrasonics is an attractive monitoring technique for damage diagnosis in large-scale plate and pipe structures. Damage can be detected by comparing incoming records with baseline records collected on intact structure. However, during long-term monitoring, environmental and operational conditions often vary significantly and produce large changes in the ultrasonic signals, thereby challenging the baseline comparison based damage detection. Researchers developed temperature compensation methods to eliminate the effects of temperature variation, but they have limitations in practical implementations. In this paper, we develop a robust damage detection method based on singular value decomposition (SVD). We show that the orthogonality of singular vectors ensures that the effect of damage and that of environmental and operational variations are separated into different singular vectors. We report on our field ultrasonic monitoring of a 273.05 mm outer diameter pipe segment, which belongs to a hot water piping system in continuous operation. We demonstrate the efficacy of our method on experimental pitch--catch records collected during seven months. We show that our method accurately detects the presence of a mass scatterer, and is robust to the environmental and operational variations exhibited in the practical system. |
Joel B Harley; José M F Moura Dispersion curve recovery with orthogonal matching pursuit Journal Article J. Acoust. Soc. Am., 137 (1), pp. EL1–EL7, 2015. @article{Harley2015-fo, title = {Dispersion curve recovery with orthogonal matching pursuit}, author = {Joel B Harley and José M F Moura}, url = {https://www.researchgate.net/publication/271536781_Dispersion_curve_recovery_with_orthogonal_matching_pursuit}, doi = {10.1121/1.4902434}, year = {2015}, date = {2015-01-01}, journal = {J. Acoust. Soc. Am.}, volume = {137}, number = {1}, pages = {EL1--EL7}, abstract = {Dispersion curves characterize many propagation mediums. When known, many methods use these curves to analyze waves. Yet, in many scenarios, their exact values are unknown due to material and environmental uncertainty. This paper presents a fast implementation of sparse wavenumber analysis, a method for recovering dispersion curves from data. This approach, based on orthogonal matching pursuit, is compared with a prior implementation, based on basis pursuit denoising. In the results, orthogonal matching pursuit provides two to three orders of magnitude improvement in speed and a small average reduction in prediction capability. The analysis is demonstrated across multiple scenarios and parameters.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Dispersion curves characterize many propagation mediums. When known, many methods use these curves to analyze waves. Yet, in many scenarios, their exact values are unknown due to material and environmental uncertainty. This paper presents a fast implementation of sparse wavenumber analysis, a method for recovering dispersion curves from data. This approach, based on orthogonal matching pursuit, is compared with a prior implementation, based on basis pursuit denoising. In the results, orthogonal matching pursuit provides two to three orders of magnitude improvement in speed and a small average reduction in prediction capability. The analysis is demonstrated across multiple scenarios and parameters. |
Christian Kexel; Joel B Harley; Jochen Moll Attenuation and phase compensation for guided wave based inspection using a filter approach Inproceedings Proc. of the IEEE International Ultrasonics Symposium, pp. 1–4, 2015. @inproceedings{Kexel2015-wg, title = {Attenuation and phase compensation for guided wave based inspection using a filter approach}, author = {Christian Kexel and Joel B Harley and Jochen Moll}, url = {https://www.researchgate.net/publication/299391590_Attenuation_and_Phase_Compensation_for_Guided_Wave_Based_Inspection_Using_a_Filter_Approach}, doi = {10.1109/ULTSYM.2015.0081}, year = {2015}, date = {2015-01-01}, booktitle = {Proc. of the IEEE International Ultrasonics Symposium}, pages = {1--4}, abstract = {Structural health monitoring (SHM) using guided waves has received great attention recently. However, detecting and characterizing defects in plate-like structures by means of guided waves is complicated due to their dispersive nature. Imaging, e.g. based on compressed sensing, benefits from dispersion compensation due to the reduced computational burden of requiring fewer atoms in the dictionary matrices. We analyze data obtained by a scanning laser doppler vibrometer (SLDV) of an isotropic aluminum plate. We propose a time-domain preprocessing step comprising the generation of a non-dispersive reference signal and a finite impulse response (FIR) filter, which reconstructs the phase (and amplitude) of the measured signal by solving a linear problem involving both signals. The reference signal is modeled as a tone-burst (defined by its center frequency and the number of cycles) and the group velocity is estimated in a data-driven manner from a training set, consisting of an early subset of the measurements acquired shortly after actuating the burst.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Structural health monitoring (SHM) using guided waves has received great attention recently. However, detecting and characterizing defects in plate-like structures by means of guided waves is complicated due to their dispersive nature. Imaging, e.g. based on compressed sensing, benefits from dispersion compensation due to the reduced computational burden of requiring fewer atoms in the dictionary matrices. We analyze data obtained by a scanning laser doppler vibrometer (SLDV) of an isotropic aluminum plate. We propose a time-domain preprocessing step comprising the generation of a non-dispersive reference signal and a finite impulse response (FIR) filter, which reconstructs the phase (and amplitude) of the measured signal by solving a linear problem involving both signals. The reference signal is modeled as a tone-burst (defined by its center frequency and the number of cycles) and the group velocity is estimated in a data-driven manner from a training set, consisting of an early subset of the measurements acquired shortly after actuating the burst. |
Joel B Harley; Chang Liu; Irving J Oppenheim; David W Greve; José M F Moura Coherent, data-driven Lamb wave localization under environmental variations Inproceedings Dale E Chimenti; Leonard J Bond (Ed.): pp. 202–210, Proc. of the Review of Quantitative Nondestructive Evaluation, 2015. @inproceedings{Harley2015-zc, title = {Coherent, data-driven Lamb wave localization under environmental variations}, author = {Joel B Harley and Chang Liu and Irving J Oppenheim and David W Greve and José M F Moura}, editor = {Dale E Chimenti and Leonard J Bond}, url = {https://www.researchgate.net/publication/299391839_Coherent_Data-Driven_Lamb_Wave_Localization_under_Environmental_Variations}, doi = {10.1063/1.4914611}, year = {2015}, date = {2015-00-01}, journal = {AIP Conf. Proc.}, volume = {1650}, number = {1}, pages = {202--210}, publisher = {Proc. of the Review of Quantitative Nondestructive Evaluation}, abstract = {Lamb waves are powerful tools in nondestructive evaluation and structural health monitoring. Researchers use Lamb waves to detect and locate damage across large areas. To best utilize Lamb waves, they are analyzed through two processing steps: baseline subtraction and velocity calibration. Baseline subtraction removes background information from our data and velocity calibration tunes our algorithms. Yet, in many scenarios, these steps are challenging to implement. Baseline subtraction is challenging due to variable environmental conditions. Velocity calibration is challenging due to multi-modal and dispersive velocity behavior in Lamb waves. To address both challenges, we present two approaches that combine environmental compensation with self-calibrating localization. We discuss temperature compensation strategies based on the scale transform and singular value decomposition. We then integrate these with a localization framework known as data-driven matched field processing. We show these combined approaches to be effective in a variety of scenarios.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Lamb waves are powerful tools in nondestructive evaluation and structural health monitoring. Researchers use Lamb waves to detect and locate damage across large areas. To best utilize Lamb waves, they are analyzed through two processing steps: baseline subtraction and velocity calibration. Baseline subtraction removes background information from our data and velocity calibration tunes our algorithms. Yet, in many scenarios, these steps are challenging to implement. Baseline subtraction is challenging due to variable environmental conditions. Velocity calibration is challenging due to multi-modal and dispersive velocity behavior in Lamb waves. To address both challenges, we present two approaches that combine environmental compensation with self-calibrating localization. We discuss temperature compensation strategies based on the scale transform and singular value decomposition. We then integrate these with a localization framework known as data-driven matched field processing. We show these combined approaches to be effective in a variety of scenarios. |
2014 |
Joel B Harley Data-Driven, Sparsity-Based Matched Field Processing for Structural Health Monitoring PhD Thesis Carnegie Mellon University, 2014. @phdthesis{Harley2014-sa, title = {Data-Driven, Sparsity-Based Matched Field Processing for Structural Health Monitoring}, author = {Joel B Harley}, url = {http://repository.cmu.edu/dissertations/392/}, year = {2014}, date = {2014-11-06}, school = {Carnegie Mellon University}, abstract = {This dissertation develops a robust, data-driven localizationmethodology based on the integration of matched field processing with compressed sensing ℓ1 recovery techniques and scale transform signal processing. The localization methodology is applied to an ultrasonic guided wave structural health monitoring system for de- tecting, locating, and imaging damage in civil infrastructures. In these systems, the channels are characterized by complex, multi-modal, and frequency dispersive wave propagation,which severely distort propagating signals. Acquiring the charac- teristics of these propagationmediums from data represents a difficult inverse prob- lem for which, in general, no readily available solution exists. In this dissertation, we build data-drivenmodels of these complexmediums by integrating experimental guidedwavemeasurementswith theoreticalwave propagationmodels and ℓ1 sparse recoverymethods from compressed sensing. The data-drivenmodels are combined with matched field processing, a localization framework extensively studied for un- derwater acoustics, to localize targets in complex, guided wave environments. The data-driven matched field processing methodology is then refined, through the use of the scale transform, to achieve robustness to environmental variations that distort guided waves. Data-driven matched field processing is experimentally applied to an ultrasound structural health monitoring system to detect and locate damage in aluminum plate structures.}, keywords = {}, pubstate = {published}, tppubtype = {phdthesis} } This dissertation develops a robust, data-driven localizationmethodology based on the integration of matched field processing with compressed sensing ℓ1 recovery techniques and scale transform signal processing. The localization methodology is applied to an ultrasonic guided wave structural health monitoring system for de- tecting, locating, and imaging damage in civil infrastructures. In these systems, the channels are characterized by complex, multi-modal, and frequency dispersive wave propagation,which severely distort propagating signals. Acquiring the charac- teristics of these propagationmediums from data represents a difficult inverse prob- lem for which, in general, no readily available solution exists. In this dissertation, we build data-drivenmodels of these complexmediums by integrating experimental guidedwavemeasurementswith theoreticalwave propagationmodels and ℓ1 sparse recoverymethods from compressed sensing. The data-drivenmodels are combined with matched field processing, a localization framework extensively studied for un- derwater acoustics, to localize targets in complex, guided wave environments. The data-driven matched field processing methodology is then refined, through the use of the scale transform, to achieve robustness to environmental variations that distort guided waves. Data-driven matched field processing is experimentally applied to an ultrasound structural health monitoring system to detect and locate damage in aluminum plate structures. |
Pong Gong; Martin E Patton; Chang Liu; Irving J Oppenheim; David W Greve; Joel B Harley; Warren R Junker Ultrasonic detection of the alkali-silica reaction damage in concrete Inproceedings Proc. of the IEEE International Ultrasonics Symposium, pp. 361–364, 2014. @inproceedings{Gong2014-dw, title = {Ultrasonic detection of the alkali-silica reaction damage in concrete}, author = {Pong Gong and Martin E Patton and Chang Liu and Irving J Oppenheim and David W Greve and Joel B Harley and Warren R Junker}, url = {https://www.researchgate.net/publication/286562817_Ultrasonic_detection_of_the_alkali-silica_reaction_damage_in_concrete}, doi = {10.1109/ULTSYM.2014.0089}, year = {2014}, date = {2014-09-01}, booktitle = {Proc. of the IEEE International Ultrasonics Symposium}, pages = {361--364}, abstract = {The alkali-silica reaction is a source of damage in concrete, which can cause serious expansion, cracking, and sometimes the failure of structures. In this paper, we use three ultrasonic methods, the attenuation spectrum method, the ultrasonic passband method, and the stretching factor method to detect the existence of alkali-silica reaction (ASR) damage and to track its progress in concrete. From our tests, we demonstrate the ultrasonic passband method and stretching factor method have performed well to achieve the above goals.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The alkali-silica reaction is a source of damage in concrete, which can cause serious expansion, cracking, and sometimes the failure of structures. In this paper, we use three ultrasonic methods, the attenuation spectrum method, the ultrasonic passband method, and the stretching factor method to detect the existence of alkali-silica reaction (ASR) damage and to track its progress in concrete. From our tests, we demonstrate the ultrasonic passband method and stretching factor method have performed well to achieve the above goals. |
Joel B Harley; José M F Moura Matched field processing localization with random sensor topologies Inproceedings Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1404–1408, IEEE, Florence, 2014. @inproceedings{Harley2014-ko, title = {Matched field processing localization with random sensor topologies}, author = {Joel B Harley and José M F Moura}, url = {https://www.researchgate.net/publication/269295254_Matched_field_processing_localization_with_random_sensor_topologies}, doi = {10.1109/ICASSP.2014.6853828}, year = {2014}, date = {2014-05-01}, booktitle = {Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing}, pages = {1404--1408}, publisher = {IEEE}, address = {Florence}, abstract = {One of the largest challenges for multichannel localization systems is developing methodologies that are robust to interference. Unlike noise, interference is not random and often has characteristics resembling the true signals of interest. Interference often originates from multipath propagation, jamming signals, or other sources. In this paper, we demonstrate that we can significantly improve localization performance in the presence of interference through the use of a random sensor topology and matched field processing. To show this, we apply concepts and results from random matrix theory and compressed sensing. We demonstrate theoretically that random sensor topologies allow us to achieve performance characteristics similar to those of random noise. Specifically, we show that the localization performance improves, with a high probability, at a rate proportional to the number of sensors in the system. We verify these results through simulation.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } One of the largest challenges for multichannel localization systems is developing methodologies that are robust to interference. Unlike noise, interference is not random and often has characteristics resembling the true signals of interest. Interference often originates from multipath propagation, jamming signals, or other sources. In this paper, we demonstrate that we can significantly improve localization performance in the presence of interference through the use of a random sensor topology and matched field processing. To show this, we apply concepts and results from random matrix theory and compressed sensing. We demonstrate theoretically that random sensor topologies allow us to achieve performance characteristics similar to those of random noise. Specifically, we show that the localization performance improves, with a high probability, at a rate proportional to the number of sensors in the system. We verify these results through simulation. |
Chang Liu; Joel B Harley; Mario Bergés; David W Greve; Warren R Junker; Irving J Oppenheim A robust baseline removal method for guided wave damage localization Inproceedings Jerome P Lynch; Kon-Well Wang; Hoon Sohn (Ed.): Proc. of SPIE Conference on Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, pp. 90611K, San Diego, CA, 2014. @inproceedings{Liu2014-tp, title = {A robust baseline removal method for guided wave damage localization}, author = {Chang Liu and Joel B Harley and Mario Bergés and David W Greve and Warren R Junker and Irving J Oppenheim}, editor = {Jerome P Lynch and Kon-Well Wang and Hoon Sohn}, url = {https://www.researchgate.net/publication/269323242_A_robust_baseline_removal_method_for_guided_wave_damage_localization}, doi = {10.1117/12.2045577}, year = {2014}, date = {2014-04-01}, booktitle = {Proc. of SPIE Conference on Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems}, pages = {90611K}, address = {San Diego, CA}, abstract = {Guided waves can propagate long distances and are sensitive to subtle structural damage. Guided-wave based damage localization often requires extracting the scatter signal(s) produced by damage, which is typically obtained by subtracting an intact baseline record from a record to be tested. However, in practical applications, environmental and operational conditions (EOC) dramatically affect guided wave signals. In this case, the baseline subtraction process can no longer perfectly remove the baseline, thereby defeating localization algorithms. In previous work, we showed that singular value decomposition (SVD) can be used to detect the presence of damage under large EOC variations, because it can differentiate the trends of damage from other EOC variations. This capability of differentiation implies that SVD can also robustly extract a scatter signal, originating from damage in the structure, that is not affected by temperature variation. This process allows us to extract a scatterer signal without the challenges associated with traditional temperature compensation and baseline subtraction routines. . In this work, we use to approach to localize structural damage in large, spatially and temporally varying EOCs. We collect pitch-catch records from randomly placed PZT transducers on an aluminum plate while undergoing temperature variations. Damage is introduced to the plate during the monitoring period. We then use our SVD method to extract the scatter signal from the records, and use the scatter signal to localize damage using the delay-and-sum method. To compare results, we also apply several temperature compensation methods to the records and then perform baseline subtraction. We show that our SVD-based approach successfully localize damage while current temperature-compensated baseline subtraction methods fail. copyright (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Guided waves can propagate long distances and are sensitive to subtle structural damage. Guided-wave based damage localization often requires extracting the scatter signal(s) produced by damage, which is typically obtained by subtracting an intact baseline record from a record to be tested. However, in practical applications, environmental and operational conditions (EOC) dramatically affect guided wave signals. In this case, the baseline subtraction process can no longer perfectly remove the baseline, thereby defeating localization algorithms. In previous work, we showed that singular value decomposition (SVD) can be used to detect the presence of damage under large EOC variations, because it can differentiate the trends of damage from other EOC variations. This capability of differentiation implies that SVD can also robustly extract a scatter signal, originating from damage in the structure, that is not affected by temperature variation. This process allows us to extract a scatterer signal without the challenges associated with traditional temperature compensation and baseline subtraction routines. . In this work, we use to approach to localize structural damage in large, spatially and temporally varying EOCs. We collect pitch-catch records from randomly placed PZT transducers on an aluminum plate while undergoing temperature variations. Damage is introduced to the plate during the monitoring period. We then use our SVD method to extract the scatter signal from the records, and use the scatter signal to localize damage using the delay-and-sum method. To compare results, we also apply several temperature compensation methods to the records and then perform baseline subtraction. We show that our SVD-based approach successfully localize damage while current temperature-compensated baseline subtraction methods fail. copyright (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
Peng Gong; Mark E Patton; David W Greve; Joel B Harley; Warren R Junker; Chang Liu; Irving J Oppenheim ASR damage detection in concrete from ultrasonic methods Inproceedings Proc. of the SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, pp. 90610E–90610E–11, International Society for Optics and Photonics, 2014. @inproceedings{Gong2014-ik, title = {ASR damage detection in concrete from ultrasonic methods}, author = {Peng Gong and Mark E Patton and David W Greve and Joel B Harley and Warren R Junker and Chang Liu and Irving J Oppenheim}, url = {https://www.researchgate.net/publication/269323365_ASR_damage_detection_in_concrete_from_ultrasonic_methods}, doi = {10.1117/12.2045645}, year = {2014}, date = {2014-03-01}, booktitle = {Proc. of the SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring}, pages = {90610E--90610E--11}, publisher = {International Society for Optics and Photonics}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Joel B Harley; José M F Moura Data-driven matched field processing for Lamb wave structural health monitoring Journal Article J. Acoust. Soc. Am., 135 (3), pp. 1231–1244, 2014. @article{Harley2014-mc, title = {Data-driven matched field processing for Lamb wave structural health monitoring}, author = {Joel B Harley and José M F Moura}, url = {https://www.researchgate.net/publication/260643917_Data-driven_matched_field_processing_for_Lamb_wave_structural_health_monitoring}, doi = {10.1121/1.4863651}, year = {2014}, date = {2014-03-01}, journal = {J. Acoust. Soc. Am.}, volume = {135}, number = {3}, pages = {1231--1244}, abstract = {Matched field processing is a model-based framework for localizing targets in complex propagation environments. In underwater acoustics, it has been extensively studied for improving localization performance in multimodal and multipath media. For guided wave structural health monitoring problems, matched field processing has not been widely applied but is an attractive option for damage localization due to equally complex propagation environments. Although effective, matched field processing is often challenging to implement because it requires accurate models of the propagation environment, and the optimization methods used to generate these models are often unreliable and computationally expensive. To address these obstacles, this paper introduces data-driven matched field processing, a framework to build models of multimodal propagation environments directly from measured data, and then use these models for localization. This paper presents the data-driven framework, analyzes its behavior under unmodeled multipath interference, and demonstrates its localization performance by distinguishing two nearby scatterers from experimental measurements of an aluminum plate. Compared with delay-based models that are commonly used in structural health monitoring, the data-driven matched field processing framework is shown to successfully localize two nearby scatterers with significantly smaller localization errors and finer resolutions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Matched field processing is a model-based framework for localizing targets in complex propagation environments. In underwater acoustics, it has been extensively studied for improving localization performance in multimodal and multipath media. For guided wave structural health monitoring problems, matched field processing has not been widely applied but is an attractive option for damage localization due to equally complex propagation environments. Although effective, matched field processing is often challenging to implement because it requires accurate models of the propagation environment, and the optimization methods used to generate these models are often unreliable and computationally expensive. To address these obstacles, this paper introduces data-driven matched field processing, a framework to build models of multimodal propagation environments directly from measured data, and then use these models for localization. This paper presents the data-driven framework, analyzes its behavior under unmodeled multipath interference, and demonstrates its localization performance by distinguishing two nearby scatterers from experimental measurements of an aluminum plate. Compared with delay-based models that are commonly used in structural health monitoring, the data-driven matched field processing framework is shown to successfully localize two nearby scatterers with significantly smaller localization errors and finer resolutions. |
Joel B Harley; Jose M F Moura Temperature Compensation in Wave-Based Damage Detection Systems Patent 20140025316:A1, 2014. @patent{Harley2014-ys, title = {Temperature Compensation in Wave-Based Damage Detection Systems}, author = {Joel B Harley and Jose M F Moura}, url = {https://patents.google.com/patent/US20140025316A1/en}, year = {2014}, date = {2014-00-01}, number = {20140025316:A1}, abstract = {A method performed by a processing device, the method comprising: obtaining first waveform data indicative of traversal of a first signal through a structure at a first time; applying a scale transform to the first waveform data and the second waveform data; computing, by the processing device and based on applying the scale transform, a scale-cross correlation function that promotes identification of scaling behavior between the first waveform data and the second waveform data; performing one or more of: computing, by the processing device and based on the scale-cross correlation function, a scale factor for the first waveform data and the second waveform data; and computing, by the processing device and based on the scale-cross correlation function, a scale invariant correlation coefficient between the first waveform data and the second waveform data.}, keywords = {}, pubstate = {published}, tppubtype = {patent} } A method performed by a processing device, the method comprising: obtaining first waveform data indicative of traversal of a first signal through a structure at a first time; applying a scale transform to the first waveform data and the second waveform data; computing, by the processing device and based on applying the scale transform, a scale-cross correlation function that promotes identification of scaling behavior between the first waveform data and the second waveform data; performing one or more of: computing, by the processing device and based on the scale-cross correlation function, a scale factor for the first waveform data and the second waveform data; and computing, by the processing device and based on the scale-cross correlation function, a scale invariant correlation coefficient between the first waveform data and the second waveform data. |
2013 |
Yujie Ying; James H Garrett; Irving J Oppenheim; Lucio Soibelman; Joel B Harley; Jun Shi; Yuanwei Jin Toward data-driven structural health monitoring: application of machine learning and signal processing to damage detection Journal Article J. Comput. Civ. Eng., 27 (6), pp. 667–680, 2013. @article{Ying2013-ha, title = {Toward data-driven structural health monitoring: application of machine learning and signal processing to damage detection}, author = {Yujie Ying and James H Garrett and Irving J Oppenheim and Lucio Soibelman and Joel B Harley and Jun Shi and Yuanwei Jin}, url = {https://www.researchgate.net/publication/259287461_Toward_Data-Driven_Structural_Health_Monitoring_Application_of_Machine_Learning_and_Signal_Processing_to_Damage_Detection}, doi = {10.1061/(ASCE)CP.1943-5487.0000258}, year = {2013}, date = {2013-11-01}, journal = {J. Comput. Civ. Eng.}, volume = {27}, number = {6}, pages = {667--680}, abstract = {A multilayer data-driven framework for robust structural health monitoring based on a comprehensive application of machine learning and signal processing techniques is introduced. This paper focuses on demonstrating the effectiveness of the framework for damage detection in a steel pipe under environmental and operational variations. The pipe was instrumented with piezoelectric wafers that can generate and sense ultrasonic waves. Damage was simulated physically by a mass scatterer grease-coupled to the surface of the pipe. Benign variations included variable internal air pressure and ambient temperature over time. Ultrasonic measurements were taken on three different days with the scatterer placed at different locations on the pipe. The wave patterns are complex and difficult to interpret, and it is even more difficult to differentiate the changes produced by the scatterer from the changes produced by benign variations. The sensed data were characterized by 365 features extracted from a variety of signal-processing techniques. Automated feature selection methods were then developed using an adaptive boosting algorithm to identify the most effective features for damage detection. With the selected features, five machine-learning classifiers were formulated based on adaptive boosting and support vector machines and achieved 98.5--99.8% average accuracy during random testing and 84.2--89% average accuracy during systematic testing. In addition, other metrics for classifier evaluation generated from a confusion matrix and from a receiver operating characteristic curve are reported.}, keywords = {}, pubstate = {published}, tppubtype = {article} } A multilayer data-driven framework for robust structural health monitoring based on a comprehensive application of machine learning and signal processing techniques is introduced. This paper focuses on demonstrating the effectiveness of the framework for damage detection in a steel pipe under environmental and operational variations. The pipe was instrumented with piezoelectric wafers that can generate and sense ultrasonic waves. Damage was simulated physically by a mass scatterer grease-coupled to the surface of the pipe. Benign variations included variable internal air pressure and ambient temperature over time. Ultrasonic measurements were taken on three different days with the scatterer placed at different locations on the pipe. The wave patterns are complex and difficult to interpret, and it is even more difficult to differentiate the changes produced by the scatterer from the changes produced by benign variations. The sensed data were characterized by 365 features extracted from a variety of signal-processing techniques. Automated feature selection methods were then developed using an adaptive boosting algorithm to identify the most effective features for damage detection. With the selected features, five machine-learning classifiers were formulated based on adaptive boosting and support vector machines and achieved 98.5--99.8% average accuracy during random testing and 84.2--89% average accuracy during systematic testing. In addition, other metrics for classifier evaluation generated from a confusion matrix and from a receiver operating characteristic curve are reported. |
Joel B Harley; Chang Liu; Irving J Oppenheim; José M F Moura High resolution localization with Lamb wave sparse wavenumber analysis Inproceedings Fu-Kuo Chang (Ed.): Proc. of the International Workshop on Structural Health Monitoring, Stanford, CA, 2013. @inproceedings{Harley2013-ou, title = {High resolution localization with Lamb wave sparse wavenumber analysis}, author = {Joel B Harley and Chang Liu and Irving J Oppenheim and José M F Moura}, editor = {Fu-Kuo Chang}, url = {https://www.researchgate.net/publication/259287299_High_Resolution_Localization_with_Lamb_Wave_Sparse_Wavenumber_Analysis}, year = {2013}, date = {2013-09-01}, booktitle = {Proc. of the International Workshop on Structural Health Monitoring}, address = {Stanford, CA}, abstract = {Guided wave structural health monitoring techniques have grown in popularity due to their ability to interrogate large areas at once and their sensitivity to damage in structures. However, guided waves are inherently complex due to their dispersive and multi-modal characteristics. These characteristics also change with variations in environmental conditions. As a result, many sophisticated localization algorithms, which rely on precise knowledge of the medium, fail to successfully locate damage. Alternatively, many current localization approaches preprocess data to simplify the measurements and reduce the adverse effects of dispersion and multiple modes. Often, these approaches only consider the first arriving wave mode across a narrow band of frequencies. They also reduce the effects of dispersion by analyzing the envelope of the received signals rather than the raw data. While these preprocessing steps may help to improve localization accuracy, they significantly degrade the resulting resolution and image quality. In this paper, we integrate a methodology known as sparse wavenumber analysis with current localization algorithms to utilize the multiple modes and dispersive characteristics of Lamb waves in a plate across a wide band of frequencies to localize damage without computing envelopes or performing similar preprocessing steps.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Guided wave structural health monitoring techniques have grown in popularity due to their ability to interrogate large areas at once and their sensitivity to damage in structures. However, guided waves are inherently complex due to their dispersive and multi-modal characteristics. These characteristics also change with variations in environmental conditions. As a result, many sophisticated localization algorithms, which rely on precise knowledge of the medium, fail to successfully locate damage. Alternatively, many current localization approaches preprocess data to simplify the measurements and reduce the adverse effects of dispersion and multiple modes. Often, these approaches only consider the first arriving wave mode across a narrow band of frequencies. They also reduce the effects of dispersion by analyzing the envelope of the received signals rather than the raw data. While these preprocessing steps may help to improve localization accuracy, they significantly degrade the resulting resolution and image quality. In this paper, we integrate a methodology known as sparse wavenumber analysis with current localization algorithms to utilize the multiple modes and dispersive characteristics of Lamb waves in a plate across a wide band of frequencies to localize damage without computing envelopes or performing similar preprocessing steps. |
Chang Liu; Joel B Harley; Nicholas O'Donoughue; Yujie Ying; Mario Bergés; Martin H Altschul; James H Garrett Jr; David Greve; José M F Moura; Irving J Oppenheim; Lucio Soibelman Ultrasonic scatterer detection in a pipe under operating conditions using singular value decomposition Inproceedings Proc. of the Review of Progress in Quantitative Nondestructive Evaluation, pp. 1454–1461, Denver, CO, 2013. @inproceedings{Liu2013-cg, title = {Ultrasonic scatterer detection in a pipe under operating conditions using singular value decomposition}, author = {Chang Liu and Joel B Harley and Nicholas O'Donoughue and Yujie Ying and Mario Bergés and Martin H Altschul and James H Garrett Jr and David Greve and José M F Moura and Irving J Oppenheim and Lucio Soibelman}, url = {https://www.researchgate.net/publication/258739204_Ultrasonic_scatterer_detection_in_a_pipe_under_operating_conditions_using_singular_value_decomposition}, doi = {10.1063/1.4789213}, year = {2013}, date = {2013-07-01}, booktitle = {Proc. of the Review of Progress in Quantitative Nondestructive Evaluation}, volume = {1454}, pages = {1454--1461}, address = {Denver, CO}, abstract = {Pipes carrying fluids under pressure are critical components in infrastructure and industry. Changes in ultrasonic signals detected by piezoelectric transducers can indicate scattering from flaws, but signals also change dramatically from environmental and operational variations. Extensive pitch-catch tests are performed on pressurized pipe segments in a working hot-water supply system that experiences ongoing variations in pressure, temperature, and flow rate. Singular value decomposition is applied to differentiate the change caused by scatterer from the changes produced by benign variations. We build a singular value decomposition (SVD) based change detector that is sensitive to the mass scatterer but insensitive to the changes produced by operational and environmental variations, and we show examples of its successful performance on field experiments data.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Pipes carrying fluids under pressure are critical components in infrastructure and industry. Changes in ultrasonic signals detected by piezoelectric transducers can indicate scattering from flaws, but signals also change dramatically from environmental and operational variations. Extensive pitch-catch tests are performed on pressurized pipe segments in a working hot-water supply system that experiences ongoing variations in pressure, temperature, and flow rate. Singular value decomposition is applied to differentiate the change caused by scatterer from the changes produced by benign variations. We build a singular value decomposition (SVD) based change detector that is sensitive to the mass scatterer but insensitive to the changes produced by operational and environmental variations, and we show examples of its successful performance on field experiments data. |
Joel B Harley; José M F Moura Broadband localization in a dispersive medium through sparse wavenumber analysis Inproceedings Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4071–4075, Vancouver, BC, 2013. @inproceedings{Harley2013-fo, title = {Broadband localization in a dispersive medium through sparse wavenumber analysis}, author = {Joel B Harley and José M F Moura}, url = {https://www.researchgate.net/publication/261153352_Broadband_localization_in_a_dispersive_medium_through_sparse_wavenumber_analysis}, doi = {10.1109/ICASSP.2013.6638424}, year = {2013}, date = {2013-05-01}, booktitle = {Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing}, pages = {4071--4075}, address = {Vancouver, BC}, abstract = {Matched field processing is a powerful tool for accurately localizing targets in dispersive media. However, matched field processing requires a precise model of the medium under test. In underwater acoustics, where matched field processing has been extensively studied, authors often resort to extremely detailed numerical models of the propagation medium, which are computationally expensive and impractical for many applications. As an alternative, this paper uses convex sparse recovery techniques to construct, directly from measured data, an accurate model of a plate medium based on its dispersion characteristics. From this data-driven model, the Green's function between two points can be readily predicted. We demonstrate the effectiveness of this model by localizing a source in a dispersive plate medium. The results visually illustrate our approach to significantly improve localization accuracy and reduce artifacts when compared to a conventional narrowband technique.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Matched field processing is a powerful tool for accurately localizing targets in dispersive media. However, matched field processing requires a precise model of the medium under test. In underwater acoustics, where matched field processing has been extensively studied, authors often resort to extremely detailed numerical models of the propagation medium, which are computationally expensive and impractical for many applications. As an alternative, this paper uses convex sparse recovery techniques to construct, directly from measured data, an accurate model of a plate medium based on its dispersion characteristics. From this data-driven model, the Green's function between two points can be readily predicted. We demonstrate the effectiveness of this model by localizing a source in a dispersive plate medium. The results visually illustrate our approach to significantly improve localization accuracy and reduce artifacts when compared to a conventional narrowband technique. |
Siheng Chen; Fernando Cerda; Jia Guo; Joel B Harley; Qing Shi; Piervincenzo Rizzo; Jacobo Bielak; James H Garrett; Jelena Kovacevic Multiresolution classification with semi-supervised learning for indirect bridge structural health monitoring Inproceedings Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3412–3416, IEEE, Vancouver, BC, 2013. @inproceedings{Chen2013-os, title = {Multiresolution classification with semi-supervised learning for indirect bridge structural health monitoring}, author = {Siheng Chen and Fernando Cerda and Jia Guo and Joel B Harley and Qing Shi and Piervincenzo Rizzo and Jacobo Bielak and James H Garrett and Jelena Kovacevic}, url = {https://www.researchgate.net/publication/259287458_Multiresolution_classification_with_semi-supervised_learning_for_indirect_bridge_structural_health_monitoring}, doi = {10.1109/ICASSP.2013.6638291}, year = {2013}, date = {2013-05-01}, booktitle = {Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing}, pages = {3412--3416}, publisher = {IEEE}, address = {Vancouver, BC}, abstract = {We present a multiresolution classification framework with semi-supervised learning for the indirect structural health mon- itoring of bridges. The monitoring approach envisions a sens- ing system embedded into a moving vehicle traveling across the bridge of interest to measure the modal characteristics of the bridge. To enhance the reliability of the sensing system, we use a semi-supervised learning algorithm and a semi-su- pervised weighting algorithm within a multiresolution clas- sification framework. We show that the proposed algorithm performs significantly better than supervised multiresolution classification. Index}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We present a multiresolution classification framework with semi-supervised learning for the indirect structural health mon- itoring of bridges. The monitoring approach envisions a sens- ing system embedded into a moving vehicle traveling across the bridge of interest to measure the modal characteristics of the bridge. To enhance the reliability of the sensing system, we use a semi-supervised learning algorithm and a semi-su- pervised weighting algorithm within a multiresolution clas- sification framework. We show that the proposed algorithm performs significantly better than supervised multiresolution classification. Index |
Chang Liu; Joel B Harley; Yujie Ying; Martin H Altschul; Mario Bergés; James H Garrett Jr.; David W Greve; José M F Moura; Irving J Oppenheim; Lucio Soibelman Ultrasonic monitoring of a pressurized pipe in operation Inproceedings Proc. of the Structures Congress, pp. 1903–1913, American Society of Civil Engineers, Reston, VA, 2013. @inproceedings{Liu2013-ch, title = {Ultrasonic monitoring of a pressurized pipe in operation}, author = {Chang Liu and Joel B Harley and Yujie Ying and Martin H Altschul and Mario Bergés and James H Garrett Jr. and David W Greve and José M F Moura and Irving J Oppenheim and Lucio Soibelman}, url = {https://www.researchgate.net/publication/259287515_Ultrasonic_Monitoring_of_a_Pressurized_Pipe_in_Operation}, doi = {10.1061/9780784412848.167}, year = {2013}, date = {2013-04-01}, booktitle = {Proc. of the Structures Congress}, pages = {1903--1913}, publisher = {American Society of Civil Engineers}, address = {Reston, VA}, abstract = {Pipes carrying pressurized fluids are an important part of the civil infrastructure, and structural health monitoring (SHM) could ensure structural integrity by predicting and preventing structural failures. Guided wave ultrasonics is a good candidate for use in pipe SHM because guided waves can propagate long distances and are sensitive to structural damage such as cracks and corrosion losses. However, the multi-modal and dispersive characteristics of guided waves make it difficult to interpret their arrival records. Moreover, guided waves are also sensitive to environmental and operational variations, limiting the effectiveness of ultrasonic methods to detect pipe damage in a real environment. We introduce a damage detector based on singular value decomposition (SVD) that can identify a change of interest, caused by a mass scatterer that simulates subtle damage, under realistic environmental variations. We show the effectiveness and robustness of this method on experimental data collected on a pipe segment under realistic environmental and operational variations over a time period of several months. Read More: http://ascelibrary.org/doi/abs/10.1061/9780784412848.167}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Pipes carrying pressurized fluids are an important part of the civil infrastructure, and structural health monitoring (SHM) could ensure structural integrity by predicting and preventing structural failures. Guided wave ultrasonics is a good candidate for use in pipe SHM because guided waves can propagate long distances and are sensitive to structural damage such as cracks and corrosion losses. However, the multi-modal and dispersive characteristics of guided waves make it difficult to interpret their arrival records. Moreover, guided waves are also sensitive to environmental and operational variations, limiting the effectiveness of ultrasonic methods to detect pipe damage in a real environment. We introduce a damage detector based on singular value decomposition (SVD) that can identify a change of interest, caused by a mass scatterer that simulates subtle damage, under realistic environmental variations. We show the effectiveness and robustness of this method on experimental data collected on a pipe segment under realistic environmental and operational variations over a time period of several months. Read More: http://ascelibrary.org/doi/abs/10.1061/9780784412848.167 |
Chang Liu; Joel B Harley; Yujie Ying; Irving J Oppenheim; Mario Bergés; David W Greve; James H Garrett Singular value decomposition for novelty detection in ultrasonic pipe monitoring Inproceedings Jerome P Lynch; Chung-Bang Yun; Kon-Well Wang (Ed.): Proc. of SPIE Conference on Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, pp. 86921R, San Diego, CA, 2013. @inproceedings{Liu2013-lq, title = {Singular value decomposition for novelty detection in ultrasonic pipe monitoring}, author = {Chang Liu and Joel B Harley and Yujie Ying and Irving J Oppenheim and Mario Bergés and David W Greve and James H Garrett}, editor = {Jerome P Lynch and Chung-Bang Yun and Kon-Well Wang}, url = {https://www.researchgate.net/publication/259287412_Singular_Value_Decomposition_for_Novelty_Detection_in_Ultrasonic_Pipe_Monitoring}, doi = {10.1117/12.2009891}, year = {2013}, date = {2013-04-01}, booktitle = {Proc. of SPIE Conference on Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems}, pages = {86921R}, address = {San Diego, CA}, abstract = {Guided wave ultrasonics is an attractive technique for structural health monitoring, especially on pressurized pipes. However, civil infrastructure components, including pipes, are often subject to large environmental and operational variations that prevent traditional baseline subtraction-based approaches from detecting damage. We collect ultrasonic data on a large-scale pipe segment in its normal operating conditions and observe large environmental variations. We developed a damage detection method based on singular value decomposition (SVD) that is robust to those benign variations. We further develop an online novelty detection framework based on our SVD method to detect the presence of a mass scatterer on the pipe at the same time that we collect the data. We examine the framework with both synthetic simulations and field experimental data. The results show that the framework can effectively detect the presence of a scatterer and is robust to large environmental and operational variations.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Guided wave ultrasonics is an attractive technique for structural health monitoring, especially on pressurized pipes. However, civil infrastructure components, including pipes, are often subject to large environmental and operational variations that prevent traditional baseline subtraction-based approaches from detecting damage. We collect ultrasonic data on a large-scale pipe segment in its normal operating conditions and observe large environmental variations. We developed a damage detection method based on singular value decomposition (SVD) that is robust to those benign variations. We further develop an online novelty detection framework based on our SVD method to detect the presence of a mass scatterer on the pipe at the same time that we collect the data. We examine the framework with both synthetic simulations and field experimental data. The results show that the framework can effectively detect the presence of a scatterer and is robust to large environmental and operational variations. |
Yujie Ying; James H Garrett; Joel Harley; Irving J Oppenheim; Jun Shi; Lucio Soibelman Damage detection in pipes under changing environmental conditions using embedded piezoelectric transducers and pattern recognition techniques Journal Article J. Pipeline Syst. Eng. Pract., 4 (1), pp. 17–23, 2013. @article{Ying2013-tz, title = {Damage detection in pipes under changing environmental conditions using embedded piezoelectric transducers and pattern recognition techniques}, author = {Yujie Ying and James H Garrett and Joel Harley and Irving J Oppenheim and Jun Shi and Lucio Soibelman}, url = {https://www.researchgate.net/publication/259332829_Damage_Detection_in_Pipes_under_Changing_Environmental_Conditions_Using_Embedded_Piezoelectric_Transducers_and_Pattern_Recognition_Techniques}, doi = {10.1061/(ASCE)PS.1949-1204.0000106}, year = {2013}, date = {2013-02-01}, journal = {J. Pipeline Syst. Eng. Pract.}, volume = {4}, number = {1}, pages = {17--23}, abstract = {A multilayer data-driven framework for robust structural health monitoring based on a comprehensive application of machine learning and signal processing techniques is introduced. This paper focuses on demonstrating the effectiveness of the framework for damage detection in a steel pipe under environmental and operational variations. The pipe was instrumented with piezoelectric wafers that can generate and sense ultrasonic waves. Damage was simulated physically by a mass scatterer grease-coupled to the surface of the pipe. Benign variations included variable internal air pressure and ambient temperature over time. Ultrasonic measurements were taken on three different days with the scatterer placed at different locations on the pipe. The wave patterns are complex and difficult to interpret, and it is even more difficult to differentiate the changes produced by the scatterer from the changes produced by benign variations. The sensed data were characterized by 365 features extracted from a variety of signal-processing techniques. Automated feature selection methods were then developed using an adaptive boosting algorithm to identify the most effective features for damage detection. With the selected features, five machine-learning classifiers were formulated based on adaptive boosting and support vector machines and achieved 98.5--99.8% average accuracy during random testing and 84.2--89% average accuracy during systematic testing. In addition, other metrics for classifier evaluation generated from a confusion matrix and from a receiver operating characteristic curve are reported.}, keywords = {}, pubstate = {published}, tppubtype = {article} } A multilayer data-driven framework for robust structural health monitoring based on a comprehensive application of machine learning and signal processing techniques is introduced. This paper focuses on demonstrating the effectiveness of the framework for damage detection in a steel pipe under environmental and operational variations. The pipe was instrumented with piezoelectric wafers that can generate and sense ultrasonic waves. Damage was simulated physically by a mass scatterer grease-coupled to the surface of the pipe. Benign variations included variable internal air pressure and ambient temperature over time. Ultrasonic measurements were taken on three different days with the scatterer placed at different locations on the pipe. The wave patterns are complex and difficult to interpret, and it is even more difficult to differentiate the changes produced by the scatterer from the changes produced by benign variations. The sensed data were characterized by 365 features extracted from a variety of signal-processing techniques. Automated feature selection methods were then developed using an adaptive boosting algorithm to identify the most effective features for damage detection. With the selected features, five machine-learning classifiers were formulated based on adaptive boosting and support vector machines and achieved 98.5--99.8% average accuracy during random testing and 84.2--89% average accuracy during systematic testing. In addition, other metrics for classifier evaluation generated from a confusion matrix and from a receiver operating characteristic curve are reported. |
Joel B Harley; Nattamon Thavornpitak; José M F Moura Delay-and-sum technique for localization of active sources in cylindrical objects Inproceedings Proc. of the Review of Progress in Nondestructive Evaluation, pp. 294–301, Proc. of the Review of Quantitative Nondestructive Evaluation, 2013. @inproceedings{Harley2013-ue, title = {Delay-and-sum technique for localization of active sources in cylindrical objects}, author = {Joel B Harley and Nattamon Thavornpitak and José M F Moura}, url = {https://www.researchgate.net/publication/258739106_Delay-and-sum_technique_for_localization_of_active_sources_in_cylindrical_objects}, doi = {10.1063/1.4789061}, year = {2013}, date = {2013-01-01}, booktitle = {Proc. of the Review of Progress in Nondestructive Evaluation}, volume = {1511}, pages = {294--301}, publisher = {Proc. of the Review of Quantitative Nondestructive Evaluation}, abstract = {For pipe guided wave inspection systems, it can often be difficult to achieve accurate localization performance due to the pipe's geometry. Many localization techniques focus on the first arrival for processing, but this often results in a poor circumferential resolution. Furthermore, the pipe's circular geometry generates multipath arrivals that make data interpretation difficult. In this paper, however, we utilize this multipath behavior by combining the standard delay-and-sum localization method with a simple multipath model for a pipe. Using experimental data from a transmitting source, we show that our method significantly improves circumferential resolution and reduces localization artifacts when compared with the standard delay-and-sum method.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } For pipe guided wave inspection systems, it can often be difficult to achieve accurate localization performance due to the pipe's geometry. Many localization techniques focus on the first arrival for processing, but this often results in a poor circumferential resolution. Furthermore, the pipe's circular geometry generates multipath arrivals that make data interpretation difficult. In this paper, however, we utilize this multipath behavior by combining the standard delay-and-sum localization method with a simple multipath model for a pipe. Using experimental data from a transmitting source, we show that our method significantly improves circumferential resolution and reduces localization artifacts when compared with the standard delay-and-sum method. |
Joel B Harley; José M F Moura Sparse recovery of the multimodal and dispersive characteristics of Lamb waves Journal Article J. Acoust. Soc. Am., 133 (5), pp. 2732–2745, 2013. @article{Harley2013-ls, title = {Sparse recovery of the multimodal and dispersive characteristics of Lamb waves}, author = {Joel B Harley and José M F Moura}, url = {https://www.researchgate.net/publication/236662602_Sparse_recovery_of_the_multimodal_and_dispersive_characteristics_of_Lamb_waves}, doi = {10.1121/1.4799805}, year = {2013}, date = {2013-01-01}, journal = {J. Acoust. Soc. Am.}, volume = {133}, number = {5}, pages = {2732--2745}, abstract = {Guided waves in plates, known as Lamb waves, are characterized by complex, multimodal, and frequency dispersive wave propagation, which distort signals and make their analysis difficult. Estimating these multimodal and dispersive characteristics from experimental data becomes a difficult, underdetermined inverse problem. To accurately and robustly recover these multimodal and dispersive properties, this paper presents a methodology referred to as sparse wavenumber analysis based on sparse recovery methods. By utilizing a general model for Lamb waves, waves propagating in a plate structure, and robust [script-l]1 optimization strategies, sparse wavenumber analysis accurately recovers the Lamb wave's frequency-wavenumber representation with a limited number of surface mounted transducers. This is demonstrated with both simulated and experimental data in the presence of multipath reflections. With accurate frequency-wavenumber representations, sparse wavenumber synthesis is then used to accurately remove multipath interference in each measurement and predict the responses between arbitrary points on a plate.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Guided waves in plates, known as Lamb waves, are characterized by complex, multimodal, and frequency dispersive wave propagation, which distort signals and make their analysis difficult. Estimating these multimodal and dispersive characteristics from experimental data becomes a difficult, underdetermined inverse problem. To accurately and robustly recover these multimodal and dispersive properties, this paper presents a methodology referred to as sparse wavenumber analysis based on sparse recovery methods. By utilizing a general model for Lamb waves, waves propagating in a plate structure, and robust [script-l]1 optimization strategies, sparse wavenumber analysis accurately recovers the Lamb wave's frequency-wavenumber representation with a limited number of surface mounted transducers. This is demonstrated with both simulated and experimental data in the presence of multipath reflections. With accurate frequency-wavenumber representations, sparse wavenumber synthesis is then used to accurately remove multipath interference in each measurement and predict the responses between arbitrary points on a plate. |
Joel B Harley; José M F Moura Decomposition of multipath Lamb waves with sparse wavenumber analysis for structural health monitoring Inproceedings Proc. of the IEEE Ultrasonics Symposium, pp. 675–678, IEEE, Prague, 2013. @inproceedings{Harley2013-om, title = {Decomposition of multipath Lamb waves with sparse wavenumber analysis for structural health monitoring}, author = {Joel B Harley and José M F Moura}, url = {https://www.researchgate.net/publication/259287454_Decomposition_of_multipath_Lamb_waves_with_sparse_wavenumber_analysis_for_structural_health_monitoring}, doi = {10.1109/ULTSYM.2013.0174}, year = {2013}, date = {2013-01-01}, booktitle = {Proc. of the IEEE Ultrasonics Symposium}, pages = {675--678}, publisher = {IEEE}, address = {Prague}, abstract = {Guided waves, such as Lamb waves, are attractive tools for monitoring large civil infrastructures due to their sensitivity to damage. Yet, interpreting guided wave data and identifying effects resulting from damage is often complicated by the multimodal and dispersive characteristics of guided waves and multipath interference from the medium’s boundaries. In this paper, we present a method to decompose guided waves into a collection of multipath arrivals by combining sparse wavenumber analysis, a methodology for accurately recovering multimodal and dispersive properties, with additional ℓ1 minimization tech- niques. Its application to experimental Lamb wave data shows that the estimates all correspond to expected paths}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Guided waves, such as Lamb waves, are attractive tools for monitoring large civil infrastructures due to their sensitivity to damage. Yet, interpreting guided wave data and identifying effects resulting from damage is often complicated by the multimodal and dispersive characteristics of guided waves and multipath interference from the medium’s boundaries. In this paper, we present a method to decompose guided waves into a collection of multipath arrivals by combining sparse wavenumber analysis, a methodology for accurately recovering multimodal and dispersive properties, with additional ℓ1 minimization tech- niques. Its application to experimental Lamb wave data shows that the estimates all correspond to expected paths |
2012 |
Nicholas O'Donoughue; Joel B Harley; Chang Liu; Jose M F Moura; Irving Oppenheim Maximum likelihood defect localization in a pipe using guided acoustic waves Inproceedings Proc. of the Asilomar Conference on Signals, Systems and Computers, pp. 1863–1867, IEEE, Pacific Grove, CA, 2012. @inproceedings{ODonoughue2012-dz, title = {Maximum likelihood defect localization in a pipe using guided acoustic waves}, author = {Nicholas O'Donoughue and Joel B Harley and Chang Liu and Jose M F Moura and Irving Oppenheim}, url = {https://www.researchgate.net/publication/259287457_Maximum_likelihood_defect_localization_in_a_pipe_using_guided_acoustic_waves}, doi = {10.1109/ACSSC.2012.6489360}, year = {2012}, date = {2012-11-01}, booktitle = {Proc. of the Asilomar Conference on Signals, Systems and Computers}, pages = {1863--1867}, publisher = {IEEE}, address = {Pacific Grove, CA}, abstract = {We discuss image formation using Maximum Likelihood (ML) for the localization of defects in pipes. We make use of guided waves (similar to Lamb waves in plates). We utilize a data-driven approach based on a priori measurements of the Green's function for a pre-defined number of grid points to overcome the complex modeling problem of dispersive, multi-modal guided waves in this environment. We then compute the ML estimate of reflectivity at each pixel given some received signal vector. We compare this approach to both backprojection and MUSIC imaging for the same set of reference and test data. We show that, for synthesized defects in a lab setting, all three approaches successfully image the defects. However, in situ measurements taken on an active hot water return pipe show that only Maximum Likelihood imaging is successful in a realistic operational environment.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We discuss image formation using Maximum Likelihood (ML) for the localization of defects in pipes. We make use of guided waves (similar to Lamb waves in plates). We utilize a data-driven approach based on a priori measurements of the Green's function for a pre-defined number of grid points to overcome the complex modeling problem of dispersive, multi-modal guided waves in this environment. We then compute the ML estimate of reflectivity at each pixel given some received signal vector. We compare this approach to both backprojection and MUSIC imaging for the same set of reference and test data. We show that, for synthesized defects in a lab setting, all three approaches successfully image the defects. However, in situ measurements taken on an active hot water return pipe show that only Maximum Likelihood imaging is successful in a realistic operational environment. |
Joel B Harley; José M F Moura Scale transform signal processing for optimal ultrasonic temperature compensation Journal Article IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 59 (10), pp. 2226–2236, 2012. @article{Harley2012-eu, title = {Scale transform signal processing for optimal ultrasonic temperature compensation}, author = {Joel B Harley and José M F Moura}, url = {https://www.researchgate.net/publication/233395758_Scale_Transform_Signal_Processing_for_Optimal_Ultrasonic_Temperature_Compensation}, doi = {10.1109/TUFFC.2012.2448}, year = {2012}, date = {2012-10-01}, journal = {IEEE Trans. Ultrason. Ferroelectr. Freq. Control}, volume = {59}, number = {10}, pages = {2226--2236}, abstract = {In structural health monitoring, temperature compensation is an important step to reduce systemic errors and avoid false-positive results. Several methods have been developed to accomplish temperature compensation in guided wave systems, but these techniques are often limited in computational speed. In this paper, we present a new methodology for optimal, stretch-based temperature compensation that operates on signals in the stretch factor and scale-transform domains. Using these tools, we demonstrate three algorithms for temperature compensation that show improved computational speed relative to other optimal methods. We test the performance of these algorithms using experimental guided wave data.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In structural health monitoring, temperature compensation is an important step to reduce systemic errors and avoid false-positive results. Several methods have been developed to accomplish temperature compensation in guided wave systems, but these techniques are often limited in computational speed. In this paper, we present a new methodology for optimal, stretch-based temperature compensation that operates on signals in the stretch factor and scale-transform domains. Using these tools, we demonstrate three algorithms for temperature compensation that show improved computational speed relative to other optimal methods. We test the performance of these algorithms using experimental guided wave data. |
Joel B Harley; Aurora C Schmidt; José M F Moura Accurate sparse recovery of guided wave characteristics for structural health monitoring Inproceedings Proc. of the IEEE International Ultrasonics Symposium, pp. 158–161, IEEE, Dresden, 2012. @inproceedings{Harley2012-gj, title = {Accurate sparse recovery of guided wave characteristics for structural health monitoring}, author = {Joel B Harley and Aurora C Schmidt and José M F Moura}, url = {https://www.researchgate.net/publication/259287449_Accurate_Sparse_Recovery_of_Guided_Wave_Characteristics_for_Structural_Health_Monitoring}, doi = {10.1109/ULTSYM.2012.0039}, year = {2012}, date = {2012-10-01}, booktitle = {Proc. of the IEEE International Ultrasonics Symposium}, pages = {158--161}, publisher = {IEEE}, address = {Dresden}, abstract = {Guided wave structural health monitoring systems are often characterized by multi-modal and dispersive propagation media. Accurate knowledge of guided wave characteristics could help to dramatically improve performance, but estimating this information from data is often very difficult. In this paper, we present a methodology, based on compressed sensing, that utilizes ℓ1-regularized optimization techniques to recover the sparse characteristics of the guided wavefields in the frequencywavenumber domain. Using simulated guided wave data, we demonstrate the performance of this technique and compare it to a more traditional approach, the 2-dimensional discrete Fourier transform method. We show that, with 10 sensors, our compressed sensing method successfully estimates 1000 points in a wavefield with an average correlation coefficient of more than 0.99 while the 2-dimensional discrete Fourier transform method requires more than 820 sensors to achieve the same performance.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Guided wave structural health monitoring systems are often characterized by multi-modal and dispersive propagation media. Accurate knowledge of guided wave characteristics could help to dramatically improve performance, but estimating this information from data is often very difficult. In this paper, we present a methodology, based on compressed sensing, that utilizes ℓ1-regularized optimization techniques to recover the sparse characteristics of the guided wavefields in the frequencywavenumber domain. Using simulated guided wave data, we demonstrate the performance of this technique and compare it to a more traditional approach, the 2-dimensional discrete Fourier transform method. We show that, with 10 sensors, our compressed sensing method successfully estimates 1000 points in a wavefield with an average correlation coefficient of more than 0.99 while the 2-dimensional discrete Fourier transform method requires more than 820 sensors to achieve the same performance. |
Chang Liu; Joel Harley; Nicholas O'Donoughue; Yujie Ying; Martin H Altschul; Mario Bergés; James H Garrett; David W Greve; Jose M F Moura; Irving J Oppenheim; Lucio Soibelman Robust change detection in highly dynamic guided wave signals with singular value decomposition Inproceedings Proc. of the IEEE International Ultrasonics Symposium, pp. 483–486, IEEE, Dresden, 2012. @inproceedings{Liu2012-qi, title = {Robust change detection in highly dynamic guided wave signals with singular value decomposition}, author = {Chang Liu and Joel Harley and Nicholas O'Donoughue and Yujie Ying and Martin H Altschul and Mario Bergés and James H Garrett and David W Greve and Jose M F Moura and Irving J Oppenheim and Lucio Soibelman}, url = {https://www.researchgate.net/publication/259287460_Robust_Change_Detection_in_Highly_Dynamic_Guided_Wave_Signals_with_Singular_Value_Decomposition}, doi = {10.1109/ULTSYM.2012.0120}, year = {2012}, date = {2012-10-01}, booktitle = {Proc. of the IEEE International Ultrasonics Symposium}, pages = {483--486}, publisher = {IEEE}, address = {Dresden}, abstract = {Ultrasonic guided waves are sensitive to small scatterers and can, in principle, be used to detect damage in pipe structures. However, pipes are often subjected to varying environmental and operational conditions (EOC), which can produce false positives or mask the change of interest. We apply singular value decomposition as a robust change detection method in ultrasonic signals. We test the methods on experimental data collected in a realistic highly dynamic environment, and show successful detection of a mass scatterer as a physical simulation of damage. We also compare our method to two other change detection methods that are robust to EOC.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Ultrasonic guided waves are sensitive to small scatterers and can, in principle, be used to detect damage in pipe structures. However, pipes are often subjected to varying environmental and operational conditions (EOC), which can produce false positives or mask the change of interest. We apply singular value decomposition as a robust change detection method in ultrasonic signals. We test the methods on experimental data collected in a realistic highly dynamic environment, and show successful detection of a mass scatterer as a physical simulation of damage. We also compare our method to two other change detection methods that are robust to EOC. |
Chang Liu; Joel B Harley; Nicholas O'Donoughue; Yujie Ying; Martin H Altschul; James H Garrett Jr.; José M F Moura; Irving J Oppenheim; Lucio Soibelman Ultrasonic monitoring of a pipe under operating conditions Inproceedings Masayoshi Tomizuka; Chung-Bang Yun; Jerome P Lynch (Ed.): Proc. of SPIE Conference on Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, pp. 83450B–83450B–12, San Diego, CA, 2012. @inproceedings{Liu2012-rx, title = {Ultrasonic monitoring of a pipe under operating conditions}, author = {Chang Liu and Joel B Harley and Nicholas O'Donoughue and Yujie Ying and Martin H Altschul and James H Garrett Jr. and José M F Moura and Irving J Oppenheim and Lucio Soibelman}, editor = {Masayoshi Tomizuka and Chung-Bang Yun and Jerome P Lynch}, url = {https://www.researchgate.net/publication/259287515_Ultrasonic_Monitoring_of_a_Pressurized_Pipe_in_Operation}, doi = {10.1061/9780784412848.167}, year = {2012}, date = {2012-04-01}, booktitle = {Proc. of SPIE Conference on Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems}, volume = {8345}, pages = {83450B--83450B--12}, address = {San Diego, CA}, abstract = {The paper presents experimental results of applying an ultrasonic monitoring system to a real-world operating hot-water supply system. The purpose of these experiments is to investigate the feasibility of continuous ultrasonic damage detection on pipes with permanently mounted piezoelectric transducers under environmental and operational variations. Ultrasonic guided wave is shown to be an efficient damage detector in laboratory experiments. However, environmental and operational variations produce dramatic changes in those signals, and therefore a useful signal processing approach must distinguish change caused by a scatterer from change caused by ongoing variations. We study pressurized pipe segments (10-in diameter) in a working hot-water supply system that experiences ongoing variations in pressure, temperature, and flow rate; the system is located in an environment that is mechanically and electrically noisy. We conduct pitch-catch tests, with a duration of 10 ms, between transducers located roughly 12 diameters apart. We applied different signal processing techniques to the collected data in order to investigate the ongoing environmental and operational variations and the stationarity of the signal. We present our analysis of these signals and preliminary detection results.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The paper presents experimental results of applying an ultrasonic monitoring system to a real-world operating hot-water supply system. The purpose of these experiments is to investigate the feasibility of continuous ultrasonic damage detection on pipes with permanently mounted piezoelectric transducers under environmental and operational variations. Ultrasonic guided wave is shown to be an efficient damage detector in laboratory experiments. However, environmental and operational variations produce dramatic changes in those signals, and therefore a useful signal processing approach must distinguish change caused by a scatterer from change caused by ongoing variations. We study pressurized pipe segments (10-in diameter) in a working hot-water supply system that experiences ongoing variations in pressure, temperature, and flow rate; the system is located in an environment that is mechanically and electrically noisy. We conduct pitch-catch tests, with a duration of 10 ms, between transducers located roughly 12 diameters apart. We applied different signal processing techniques to the collected data in order to investigate the ongoing environmental and operational variations and the stationarity of the signal. We present our analysis of these signals and preliminary detection results. |
Aurora Schmidt; Joel B Harley; Jose M F Moura Compressed sensing radar surveillance networks Inproceedings Proc. of the IEEE Sensor Array and Multichannel Signal Processing Workshop, pp. 209–212, IEEE, Hoboken, NJ, 2012. @inproceedings{Schmidt2012-pa, title = {Compressed sensing radar surveillance networks}, author = {Aurora Schmidt and Joel B Harley and Jose M F Moura}, url = {https://www.researchgate.net/publication/259287452_Compressed_sensing_radar_surveillance_networks}, doi = {10.1109/SAM.2012.6250469}, year = {2012}, date = {2012-01-01}, booktitle = {Proc. of the IEEE Sensor Array and Multichannel Signal Processing Workshop}, pages = {209--212}, publisher = {IEEE}, address = {Hoboken, NJ}, abstract = {We study the problem of sensor fusion in a simplified radar surveillance application. A potentially large number of narrowband radars with isotropic antennas monitor a two-dimensional area for an unknown number of targets. We use techniques from compressive sensing to distribute efficient projections of network observations, allowing for reconstruction of the target scene using a single snapshot of sensor data. We avoid the use of a fusion node, allowing all radars to individually estimate target locations after iterative communication with neighboring sensors. We study the robustness of the discretization of continuous target locations, comparing estimation performance of basis pursuit reconstruction methods to a sparse estimator based on a model-robust formulation. We test the approach on simulated scenarios, showing tradeoffs in the resolution of target localization as well as the communication bandwidths required for this inter-radar cooperation scheme.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } We study the problem of sensor fusion in a simplified radar surveillance application. A potentially large number of narrowband radars with isotropic antennas monitor a two-dimensional area for an unknown number of targets. We use techniques from compressive sensing to distribute efficient projections of network observations, allowing for reconstruction of the target scene using a single snapshot of sensor data. We avoid the use of a fusion node, allowing all radars to individually estimate target locations after iterative communication with neighboring sensors. We study the robustness of the discretization of continuous target locations, comparing estimation performance of basis pursuit reconstruction methods to a sparse estimator based on a model-robust formulation. We test the approach on simulated scenarios, showing tradeoffs in the resolution of target localization as well as the communication bandwidths required for this inter-radar cooperation scheme. |
2011 |
Joel B Harley; José M F Moura Guided wave temperature compensation with the scale-invariant correlation coefficient Inproceedings Proc. of the IEEE International Ultrasonics Symposium, pp. 1068–1071, Orlando, FL, 2011. @inproceedings{Harley2011-ms, title = {Guided wave temperature compensation with the scale-invariant correlation coefficient}, author = {Joel B Harley and José M F Moura}, url = {https://www.researchgate.net/publication/259287455_Guided_Wave_Temperature_Compensation_with_the_Scale-Invariant_Correlation_Coefficient}, doi = {10.1109/ULTSYM.2011.0218}, year = {2011}, date = {2011-10-01}, booktitle = {Proc. of the IEEE International Ultrasonics Symposium}, pages = {1068--1071}, address = {Orlando, FL}, abstract = {One of the greatest challenges toward developing guided wave structural health monitoring technology is the necessity to distinguish benign effects from those caused by damage. Variations in temperature, one of the most prominent benign effects, are known to stretch or scale ultrasonic signals in time. Several techniques have been proposed to compensate for the effects of temperature, but they tend to be computationally expensive, require locally convex conditions, or lack robustness to modeling error. In this paper, we present a new technique, based on the Mellin and scale transforms, which takes advantage of available fast algorithms for computing and compensating stretch-based operations. Using experimental data, we show our technique to be accurate, robust, and algorithmically faster than other existing techniques.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } One of the greatest challenges toward developing guided wave structural health monitoring technology is the necessity to distinguish benign effects from those caused by damage. Variations in temperature, one of the most prominent benign effects, are known to stretch or scale ultrasonic signals in time. Several techniques have been proposed to compensate for the effects of temperature, but they tend to be computationally expensive, require locally convex conditions, or lack robustness to modeling error. In this paper, we present a new technique, based on the Mellin and scale transforms, which takes advantage of available fast algorithms for computing and compensating stretch-based operations. Using experimental data, we show our technique to be accurate, robust, and algorithmically faster than other existing techniques. |
Joel B Harley; José M F Moura An efficient temperature compensation technique for guided wave ultrasonic inspection Inproceedings Proc. of the International Workshop on Structural Health Monitoring, Stanford, CA, 2011. @inproceedings{Harley2011-mx, title = {An efficient temperature compensation technique for guided wave ultrasonic inspection}, author = {Joel B Harley and José M F Moura}, url = {https://www.researchgate.net/publication/259287297_An_efficient_temperature_compensation_technique_for_guided_wave_ultrasonic_inspection}, year = {2011}, date = {2011-09-01}, booktitle = {Proc. of the International Workshop on Structural Health Monitoring}, address = {Stanford, CA}, abstract = {One challenge in the development of structural health monitoring technology is the necessity to distinguish benign effects from those caused by damage. For ultrasonic guided waves systems, this is a problem of particular importance. Guided waves create complex, multi-modal, and dispersive wave fields which reflect off specimen boundaries as well as damage. Direct time-domain comparisons with a known baseline can be used to overcome these complexities, but fail to discriminate damage from benign environmental effects. Although many environmental changes affect guided waves, variations in temperature are often the most dominant. This pa- per proposes a computationally efficient temperature compensation technique based on the scale-invariant correlation coefficient. Using experimental measurements, we compare the performance of the scale-invariant correlation coefficient with two other compensation strategies: the local peak coherence and optimal signal stretch methods. We demonstrate the scale-invariant correlation coefficient to be robust, effective, and computational efficient.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } One challenge in the development of structural health monitoring technology is the necessity to distinguish benign effects from those caused by damage. For ultrasonic guided waves systems, this is a problem of particular importance. Guided waves create complex, multi-modal, and dispersive wave fields which reflect off specimen boundaries as well as damage. Direct time-domain comparisons with a known baseline can be used to overcome these complexities, but fail to discriminate damage from benign environmental effects. Although many environmental changes affect guided waves, variations in temperature are often the most dominant. This pa- per proposes a computationally efficient temperature compensation technique based on the scale-invariant correlation coefficient. Using experimental measurements, we compare the performance of the scale-invariant correlation coefficient with two other compensation strategies: the local peak coherence and optimal signal stretch methods. We demonstrate the scale-invariant correlation coefficient to be robust, effective, and computational efficient. |
Yujie Ying; James H Garrett; Joel B Harley; M F Moura; Nicholas O'Donoughue; Irving J Oppenheim Machine Learning for Pipeline Monitoring under Environmental and Operational Variations Inproceedings Proc. of the International Workshop on Structural Health Monitoring, Stanford, CA, 2011. @inproceedings{Ying2011-he, title = {Machine Learning for Pipeline Monitoring under Environmental and Operational Variations}, author = {Yujie Ying and James H Garrett and Joel B Harley and M F Moura and Nicholas O'Donoughue and Irving J Oppenheim}, url = {https://www.researchgate.net/publication/259287411_Machine_Learning_for_Pipeline_Monitoring_under_Environmental_and_Operational_Variations}, year = {2011}, date = {2011-09-01}, booktitle = {Proc. of the International Workshop on Structural Health Monitoring}, address = {Stanford, CA}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Yujie Ying; Joel Harley; James H Garrett Jr.; Yuanwei Jin; Irving J Oppenheim; Jun Shi; Lucio Soibelman Applications of Machine Learning in Pipeline Monitoring Inproceedings Proc. of the ASCE International Workshop on Computing in Civil Engineering, pp. 242–249, ASCE, Miami, 2011. @inproceedings{Ying2011-ka, title = {Applications of Machine Learning in Pipeline Monitoring}, author = {Yujie Ying and Joel Harley and James H Garrett Jr. and Yuanwei Jin and Irving J Oppenheim and Jun Shi and Lucio Soibelman}, url = {https://www.researchgate.net/publication/259287450_Applications_of_Machine_Learning_in_Pipeline_Monitoring}, doi = {10.1061/41182(416)30}, year = {2011}, date = {2011-08-01}, booktitle = {Proc. of the ASCE International Workshop on Computing in Civil Engineering}, pages = {242--249}, publisher = {ASCE}, address = {Miami}, abstract = {In the field of structural health monitoring, researchers focus on the design of systems and techniques capable of detecting damage in structures. However, most traditional detection methods fail under environmental and operational variations that tend to distort the signals and masquerade as damage. In this paper, we investigate the applications of machine learning techniques to developing a damage detection system robust to changes in the internal air pressure of a pipe. From each of the 240 experimental datasets, we extract 167 features and implement three classification algorithms for detecting damage: adaptive boosting, support vector machines, and a method combining the two. The performances of the three classifiers are evaluated over 30 detection trials with different combinations of training and testing data, resulting in the average accuracies of 87.7%, 92.5% and 93.5%, respectively. The combined method is a promising classifier for damage detection. Through feature selection, we also demonstrate the effectiveness of features related to the curve length, the shift‐invariant correlation coefficient and the peak amplitude of the signal.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In the field of structural health monitoring, researchers focus on the design of systems and techniques capable of detecting damage in structures. However, most traditional detection methods fail under environmental and operational variations that tend to distort the signals and masquerade as damage. In this paper, we investigate the applications of machine learning techniques to developing a damage detection system robust to changes in the internal air pressure of a pipe. From each of the 240 experimental datasets, we extract 167 features and implement three classification algorithms for detecting damage: adaptive boosting, support vector machines, and a method combining the two. The performances of the three classifiers are evaluated over 30 detection trials with different combinations of training and testing data, resulting in the average accuracies of 87.7%, 92.5% and 93.5%, respectively. The combined method is a promising classifier for damage detection. Through feature selection, we also demonstrate the effectiveness of features related to the curve length, the shift‐invariant correlation coefficient and the peak amplitude of the signal. |
Joel B Harley; Yujie Ying; José M F Moura; Irving J Oppenheim; Lucio Sobelman; James H Garrett Application of Mellin transform features for robust ultrasonic guided wave structural health monitoring Inproceedings Proc. of the Review of Progress in Quantitative Nondestructive Evaluation, pp. 1551–1558, Burlington, VT, 2011. @inproceedings{Harley2011-us, title = {Application of Mellin transform features for robust ultrasonic guided wave structural health monitoring}, author = {Joel B Harley and Yujie Ying and José M F Moura and Irving J Oppenheim and Lucio Sobelman and James H Garrett}, url = {https://www.researchgate.net/publication/258570051_Application_of_Mellin_transform_features_for_robust_ultrasonic_guided_wave_structural_health_monitoring}, doi = {10.1063/1.4716399}, year = {2011}, date = {2011-07-01}, booktitle = {Proc. of the Review of Progress in Quantitative Nondestructive Evaluation}, volume = {31}, pages = {1551--1558}, address = {Burlington, VT}, abstract = {Guided wave based structural health monitoring systems are sensitive to environmental and operational conditions. This leads to false-positive results for most conventional detection methods. In this paper, we investigate the capabilities of theMellin transformfor detecting damage under variable environmental conditions. The Mellin transform is chosen due to its invariance to scaling operations and robustness to wave velocity. From experimental results, we demonstrate that the Mellin transform features can detect a mass on a steel pipe under variable internal pressure with an overall average accuracy of 94.00% while equivalent Fourier transform features detect the mass with only a 67.00% accuracy.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Guided wave based structural health monitoring systems are sensitive to environmental and operational conditions. This leads to false-positive results for most conventional detection methods. In this paper, we investigate the capabilities of theMellin transformfor detecting damage under variable environmental conditions. The Mellin transform is chosen due to its invariance to scaling operations and robustness to wave velocity. From experimental results, we demonstrate that the Mellin transform features can detect a mass on a steel pipe under variable internal pressure with an overall average accuracy of 94.00% while equivalent Fourier transform features detect the mass with only a 67.00% accuracy. |
Nicholas O'Donoughue; Joel B Harley; Jose M F Moura Detection of targets embedded in multipath clutter with time reversal Inproceedings Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3868–3871, IEEE, Prague, 2011. @inproceedings{ODonoughue2011-pa, title = {Detection of targets embedded in multipath clutter with time reversal}, author = {Nicholas O'Donoughue and Joel B Harley and Jose M F Moura}, url = {https://www.researchgate.net/publication/220735758_Detection_of_targets_embedded_in_multipath_clutter_with_Time_Reversal}, doi = {10.1109/ICASSP.2011.5947196}, year = {2011}, date = {2011-05-01}, booktitle = {Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing}, pages = {3868--3871}, publisher = {IEEE}, address = {Prague}, abstract = {Detection of targets in complex environments is of importance in both radar and sonar applications. Recent work has shown that the use of Time Reversal (TR) techniques improves the performance of systems operating in deterministic channels with a significant multipath return. This paper extends those results to stationary random channels with significant multipath. We develop a TR-based approach and derive the Likelihood Ratio Test (LRT) for this approach. We compare this TR-LRT to an LRT derived through a ``water filling'' approach. We derive theoretical performance curves for the water filling LRT, and evaluate both the water filling and TR detectors with Monte Carlo simulations. For the scenarios tested, we show that TR achieves an SNR gain of 1--2dB over the water filling detector.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Detection of targets in complex environments is of importance in both radar and sonar applications. Recent work has shown that the use of Time Reversal (TR) techniques improves the performance of systems operating in deterministic channels with a significant multipath return. This paper extends those results to stationary random channels with significant multipath. We develop a TR-based approach and derive the Likelihood Ratio Test (LRT) for this approach. We compare this TR-LRT to an LRT derived through a ``water filling'' approach. We derive theoretical performance curves for the water filling LRT, and evaluate both the water filling and TR detectors with Monte Carlo simulations. For the scenarios tested, we show that TR achieves an SNR gain of 1--2dB over the water filling detector. |
2010 |
Nicholas O'Donoughue; Joel Harley; Jose M F Moura Time reversal beamforming of guided waves in pipes with a single defect Inproceedings Proc. of the Asilomar Conference on Signals, Systems and Computers, pp. 1786–1790, IEEE, Pacific Grove, CA, 2010. @inproceedings{ODonoughue2010-mr, title = {Time reversal beamforming of guided waves in pipes with a single defect}, author = {Nicholas O'Donoughue and Joel Harley and Jose M F Moura}, year = {2010}, date = {2010-11-01}, booktitle = {Proc. of the Asilomar Conference on Signals, Systems and Computers}, pages = {1786--1790}, publisher = {IEEE}, address = {Pacific Grove, CA}, abstract = {Structural health monitoring of buried pipelines is an important application for aging infrastructures. Ultrasonic guided wave inspection is an attractive tool, due to the long propagation of guided waves in the wall of a hollow cylinder. However, guided waves present a unique environment with heavily multi-modal signal propagation and complex dispersion (frequency-dependent propagation speeds). In order to alleviate these challenges, conventional techniques rely on high-voltage excitation with complex transducer arrays, but these systems are not conducive to a monitoring solution. Instead, they require periodic excavation and testing. In prior work, we have shown that Time Reversal allows for reliable detection with relatively simple antenna arrays that can be operated in low-power. This paper focuses on localization of these defects. We utilize a beamforming approach that makes use of theoretical dispersion curves to generate fault images. We show through simulations that Time Reversal Beamforming achieves high-resolution localization of a fault in the presence of strong dispersion and heavily multi-modal propagation.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Structural health monitoring of buried pipelines is an important application for aging infrastructures. Ultrasonic guided wave inspection is an attractive tool, due to the long propagation of guided waves in the wall of a hollow cylinder. However, guided waves present a unique environment with heavily multi-modal signal propagation and complex dispersion (frequency-dependent propagation speeds). In order to alleviate these challenges, conventional techniques rely on high-voltage excitation with complex transducer arrays, but these systems are not conducive to a monitoring solution. Instead, they require periodic excavation and testing. In prior work, we have shown that Time Reversal allows for reliable detection with relatively simple antenna arrays that can be operated in low-power. This paper focuses on localization of these defects. We utilize a beamforming approach that makes use of theoretical dispersion curves to generate fault images. We show through simulations that Time Reversal Beamforming achieves high-resolution localization of a fault in the presence of strong dispersion and heavily multi-modal propagation. |
Yujie Ying; Lucio Soibelman; Joel Harley; Nicholas O'Donoughue; James H Garrett; Yuanwei Jin; José M F Moura; Irving J Oppenheim A data mining framework for pipeline monitoring using time reversal Inproceedings Proc. of SIAM Conference on Data Mining, SIAM, Columbus, Ohio, 2010. @inproceedings{Ying2010-sm, title = {A data mining framework for pipeline monitoring using time reversal}, author = {Yujie Ying and Lucio Soibelman and Joel Harley and Nicholas O'Donoughue and James H Garrett and Yuanwei Jin and José M F Moura and Irving J Oppenheim}, year = {2010}, date = {2010-04-01}, booktitle = {Proc. of SIAM Conference on Data Mining}, publisher = {SIAM}, address = {Columbus, Ohio}, abstract = {This paper presents a data mining framework under development based on Time Reversal for continuous monitoring of natural gas pipelines. Our goal is to extract damage information from complex guided wave patterns in pipes. We first review Time Reversal methods, and discuss their effectiveness and limitation for defect detection. Then, we describe our experimental results with Time Reversal change detection which show that it is able to detect small defects through its focusing effect. However, Time Reversal is sensitive to changing environmental and operational conditions, which may increase the false alarm rate. To reduce the number of false positives, we propose a data mining framework that integrates Time Reversal with data mining tools. The data mining framework consists of three modules: defect detection, defect localization, and defect classification. We explore the potential use of Time Reversal in each work module. This paper highlights these tasks and provides a clear work flow to further our pipeline monitoring research.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper presents a data mining framework under development based on Time Reversal for continuous monitoring of natural gas pipelines. Our goal is to extract damage information from complex guided wave patterns in pipes. We first review Time Reversal methods, and discuss their effectiveness and limitation for defect detection. Then, we describe our experimental results with Time Reversal change detection which show that it is able to detect small defects through its focusing effect. However, Time Reversal is sensitive to changing environmental and operational conditions, which may increase the false alarm rate. To reduce the number of false positives, we propose a data mining framework that integrates Time Reversal with data mining tools. The data mining framework consists of three modules: defect detection, defect localization, and defect classification. We explore the potential use of Time Reversal in each work module. This paper highlights these tasks and provides a clear work flow to further our pipeline monitoring research. |
Yuanwei Jin; Nicholas O'Donoughue; José M F Moura; Joel B Harley; James H Garrett; Irving J Oppenheim; Lucio Soibelman; Yujie Ying; Lin He Cognitive sensor networks for structure defect monitoring and classification using guided wave signals Inproceedings Masayoshi Tomizuka (Ed.): Proc. of SPIE Conference on Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, pp. 76473T–76473T–12, San Diego, CA, 2010. @inproceedings{Jin2010-et, title = {Cognitive sensor networks for structure defect monitoring and classification using guided wave signals}, author = {Yuanwei Jin and Nicholas O'Donoughue and José M F Moura and Joel B Harley and James H Garrett and Irving J Oppenheim and Lucio Soibelman and Yujie Ying and Lin He}, editor = {Masayoshi Tomizuka}, year = {2010}, date = {2010-03-01}, booktitle = {Proc. of SPIE Conference on Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems}, volume = {7647}, pages = {76473T--76473T--12}, address = {San Diego, CA}, abstract = {This paper develops a framework of a cognitive sensor networks system for structure defect monitoring and classification using guided wave signals. Guided ultrasonic waves that can propagate long distances along civil structures have been widely studied for inspection and detection of structure damage. Smart ultrasonic sensors arranged as a spatially distributed cognitive sensor networks system can transmit and receive ultrasonic guided waves to interrogate structure defects such as cracks and corrosion. A distinguishing characteristic of the cognitive sensor networks system is that it adaptively probes and learns about the environment, which enables constant optimization in response to its changing understanding of the defect response. In this paper, we develop a sequential multiple hypothesis testing scheme combined with adaptive waveform transmission for defect monitoring and classification. The performance is verified using numerical simulations of guided elastic wave propagation on a pipe model and by Monte Carlo simulations for computing the probability of correct classification.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper develops a framework of a cognitive sensor networks system for structure defect monitoring and classification using guided wave signals. Guided ultrasonic waves that can propagate long distances along civil structures have been widely studied for inspection and detection of structure damage. Smart ultrasonic sensors arranged as a spatially distributed cognitive sensor networks system can transmit and receive ultrasonic guided waves to interrogate structure defects such as cracks and corrosion. A distinguishing characteristic of the cognitive sensor networks system is that it adaptively probes and learns about the environment, which enables constant optimization in response to its changing understanding of the defect response. In this paper, we develop a sequential multiple hypothesis testing scheme combined with adaptive waveform transmission for defect monitoring and classification. The performance is verified using numerical simulations of guided elastic wave propagation on a pipe model and by Monte Carlo simulations for computing the probability of correct classification. |
Yuanwei Jin; José M F Moura; Nicholas O'Donoughue; Joel Harley Single antenna time reversal detection of moving target Inproceedings Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3558–3561, IEEE, Dallas, TX, 2010. @inproceedings{Jin2010-hp, title = {Single antenna time reversal detection of moving target}, author = {Yuanwei Jin and José M F Moura and Nicholas O'Donoughue and Joel Harley}, year = {2010}, date = {2010-03-01}, booktitle = {Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing}, pages = {3558--3561}, publisher = {IEEE}, address = {Dallas, TX}, abstract = {This paper is concerned with a moving target detection using time reversal in dense multipath environments. We show that the Doppler shift in the time reversal re-transmission simplifies the detector design, yet still achieves the focusing effect. Thus, the Doppler diversity is utilized to achieve high target detectability by time reversal.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper is concerned with a moving target detection using time reversal in dense multipath environments. We show that the Doppler shift in the time reversal re-transmission simplifies the detector design, yet still achieves the focusing effect. Thus, the Doppler diversity is utilized to achieve high target detectability by time reversal. |
Yujie Ying; Joel Harley; James H Garrett Jr.; Yuanwei Jin; José M F Moura; Nicholas O'Donoughue; Irving J Oppenheim; Lucio Soibelman Time reversal for damage detection in pipes Inproceedings Proc. of SPIE Conference on Smart Structures and Nondestructive Evaluation, pp. 76473S–76473S–12, SPIE, San Diego, CA, 2010. @inproceedings{Ying2010-ya, title = {Time reversal for damage detection in pipes}, author = {Yujie Ying and Joel Harley and James H Garrett Jr. and Yuanwei Jin and José M F Moura and Nicholas O'Donoughue and Irving J Oppenheim and Lucio Soibelman}, year = {2010}, date = {2010-03-01}, booktitle = {Proc. of SPIE Conference on Smart Structures and Nondestructive Evaluation}, volume = {7647}, pages = {76473S--76473S--12}, publisher = {SPIE}, address = {San Diego, CA}, abstract = {Monitoring the structural integrity of vast natural gas pipeline networks requires continuous and economical inspection technology. Current approaches for inspecting buried pipelines require periodic excavation of sections of pipe to assess only a couple of hundred meters at a time. These inspection systems for pipelines are temporary and expensive. We propose to use guided-wave ultrasonics with Time Reversal techniques to develop an active sensing and continuous monitoring system. Pipe environments are complex due to the presence of multiple modes and high dispersion. These are treated as adverse effects by most conventional ultrasonic techniques. However, Time Reversal takes advantage of the multi-modal and dispersive behaviors to improve the spatial and temporal wave focusing. In this paper, Time Reversal process is mathematically described and experimentally demonstrated through six laboratory experiments, providing comprehensive and promising results on guided wave focusing in a pipe with/without welded joint, with/without internal pressure, and detection of three defects: lateral, longitudinal and corrosion-like. The experimental results show that Time Reversal can effectively compensate for multiple modes and dispersion in pipes, resulting in an enhanced signal-to-noise ratio and effective damage detection ability. As a consequence, Time Reversal shows benefits in long-distance and lowpower pipeline monitoring, as well as potential for applications in other infrastructures.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Monitoring the structural integrity of vast natural gas pipeline networks requires continuous and economical inspection technology. Current approaches for inspecting buried pipelines require periodic excavation of sections of pipe to assess only a couple of hundred meters at a time. These inspection systems for pipelines are temporary and expensive. We propose to use guided-wave ultrasonics with Time Reversal techniques to develop an active sensing and continuous monitoring system. Pipe environments are complex due to the presence of multiple modes and high dispersion. These are treated as adverse effects by most conventional ultrasonic techniques. However, Time Reversal takes advantage of the multi-modal and dispersive behaviors to improve the spatial and temporal wave focusing. In this paper, Time Reversal process is mathematically described and experimentally demonstrated through six laboratory experiments, providing comprehensive and promising results on guided wave focusing in a pipe with/without welded joint, with/without internal pressure, and detection of three defects: lateral, longitudinal and corrosion-like. The experimental results show that Time Reversal can effectively compensate for multiple modes and dispersion in pipes, resulting in an enhanced signal-to-noise ratio and effective damage detection ability. As a consequence, Time Reversal shows benefits in long-distance and lowpower pipeline monitoring, as well as potential for applications in other infrastructures. |
Joel Harley; Nicholas O'Donoughue; Yuanwei Jin; José M F Moura Time Reversal Focusing for Pipeline Structural Health Monitoring Inproceedings Proc. of Meetings on Acoustics, pp. 030001–030008, AIP, San Antonio, TX, 2010. @inproceedings{Harley2010-nw, title = {Time Reversal Focusing for Pipeline Structural Health Monitoring}, author = {Joel Harley and Nicholas O'Donoughue and Yuanwei Jin and José M F Moura}, year = {2010}, date = {2010-01-01}, booktitle = {Proc. of Meetings on Acoustics}, volume = {8}, pages = {030001--030008}, publisher = {AIP}, address = {San Antonio, TX}, abstract = {This paper investigates the use of time reversal processing techniques to compensate for multimodal and dispersive effects in a low-power structural health monitoring system for pipelines. We demonstrate the use of time reversal as a pitch-catch operation between two transducer arrays to illuminate changes caused by damage on a pipe. We then show and discuss how differences in the severity of damage affect the signals recorded at the receiving transducer array and demonstrate how these results can be interpreted to measure those changes. Our results are demonstrated through experimental observation.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper investigates the use of time reversal processing techniques to compensate for multimodal and dispersive effects in a low-power structural health monitoring system for pipelines. We demonstrate the use of time reversal as a pitch-catch operation between two transducer arrays to illuminate changes caused by damage on a pipe. We then show and discuss how differences in the severity of damage affect the signals recorded at the receiving transducer array and demonstrate how these results can be interpreted to measure those changes. Our results are demonstrated through experimental observation. |
2009 |
Joel Harley; Nicholas O'Donoughue; Joseph States; Yujie Ying; James H Garrett; Yuanwei Jin; José M F Moura; Irving J Oppenheim; Lucio Soibelman Focusing of ultrasonic waves in cylindrical shells using time reversal Inproceedings Proc. of the International Workshop on Structural Health Monitoring, pp. 283–291, Stanford, CA, 2009. @inproceedings{Harley2009-yy, title = {Focusing of ultrasonic waves in cylindrical shells using time reversal}, author = {Joel Harley and Nicholas O'Donoughue and Joseph States and Yujie Ying and James H Garrett and Yuanwei Jin and José M F Moura and Irving J Oppenheim and Lucio Soibelman}, year = {2009}, date = {2009-09-01}, booktitle = {Proc. of the International Workshop on Structural Health Monitoring}, pages = {283--291}, address = {Stanford, CA}, abstract = {This paper investigates time reversal focusing techniques for the development of low-power, long-range, structural health monitoring applications for pipelines. We analytically examine time reversal’s ability to compensate for unwanted multimodal and dispersive behavior that are characteristic of guided waves travelling through pipes. We then develop a method to illuminate changes caused by structural damage using time reversal focusing as a pitch-catch operation. Using experimental and finite element simulation results with two transducers, we demonstrate these concepts and show that time reversal focusing provides a clear, interpretable metric for the characterization of damage in a pipe.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper investigates time reversal focusing techniques for the development of low-power, long-range, structural health monitoring applications for pipelines. We analytically examine time reversal’s ability to compensate for unwanted multimodal and dispersive behavior that are characteristic of guided waves travelling through pipes. We then develop a method to illuminate changes caused by structural damage using time reversal focusing as a pitch-catch operation. Using experimental and finite element simulation results with two transducers, we demonstrate these concepts and show that time reversal focusing provides a clear, interpretable metric for the characterization of damage in a pipe. |
Abhinav Agrawal; Joel Harley; Yujie Ying; James H Garrett; Lucio Soibelman; Hoon Sohn Preliminary studies on the dispersion of signals produced by permanently installed MFC transducers for pipeline monitoring Inproceedings Proc. of the International Workshop on Intelligent Computing in Engineering, Berlin, 2009. @inproceedings{Agrawal2009-pa, title = {Preliminary studies on the dispersion of signals produced by permanently installed MFC transducers for pipeline monitoring}, author = {Abhinav Agrawal and Joel Harley and Yujie Ying and James H Garrett and Lucio Soibelman and Hoon Sohn}, year = {2009}, date = {2009-07-01}, booktitle = {Proc. of the International Workshop on Intelligent Computing in Engineering}, address = {Berlin}, abstract = {The paper provides an initial study of the dispersion and attenuation of signals produced by permanently installed MFC transducers on pipelines for monitoring these pipelines. Currently, most systems for pipeline inspection are temporarily deployed due to their large expense, bulky transducer setup, and high power requirements. The advantages of a permanently installed system are that the pipelines on which they are installed can be monitored continuously, process data locally, and report results over a large sensor network. Important issues that have been investigated include the dispersion characteristics of the different modes generated by the MFC patches. These issues have been investigated through comparing results from experiments and simulations. Developing an accurate numerical simulation is important for predicting the potential of new defect detection techniques before investing the resources involved in performing extensive physical experiments and prototyping. Using finite element simulations, guided wave propagation in a hollow cylinder has been shown to be comparable with results from an equivalent experimental setup. The results show a positive correlation between the guided wave modes observed, dispersion characteristics, and attenuation as a function of distance.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The paper provides an initial study of the dispersion and attenuation of signals produced by permanently installed MFC transducers on pipelines for monitoring these pipelines. Currently, most systems for pipeline inspection are temporarily deployed due to their large expense, bulky transducer setup, and high power requirements. The advantages of a permanently installed system are that the pipelines on which they are installed can be monitored continuously, process data locally, and report results over a large sensor network. Important issues that have been investigated include the dispersion characteristics of the different modes generated by the MFC patches. These issues have been investigated through comparing results from experiments and simulations. Developing an accurate numerical simulation is important for predicting the potential of new defect detection techniques before investing the resources involved in performing extensive physical experiments and prototyping. Using finite element simulations, guided wave propagation in a hollow cylinder has been shown to be comparable with results from an equivalent experimental setup. The results show a positive correlation between the guided wave modes observed, dispersion characteristics, and attenuation as a function of distance. |
Nicholas O'Donoughue; Joel B Harley; Jose M Moura; Yuanwei Jin; Irving Oppenheim; Yujie Ying; Joseph States; James H Garrett; Lucio Soibelman Single antenna time reversal of guided waves in pipelines Inproceedings Proc. of Meetings on Acoustics, pp. 065001–065011, Portland, OR, 2009. @inproceedings{ODonoughue2009-se, title = {Single antenna time reversal of guided waves in pipelines}, author = {Nicholas O'Donoughue and Joel B Harley and Jose M Moura and Yuanwei Jin and Irving Oppenheim and Yujie Ying and Joseph States and James H Garrett and Lucio Soibelman}, year = {2009}, date = {2009-05-01}, booktitle = {Proc. of Meetings on Acoustics}, volume = {6}, pages = {065001--065011}, address = {Portland, OR}, abstract = {The volatile nature of natural gas makes it extremely important to ensure that distribution pipelines remain free from defects, as leakage can result in explosions. Many current methods for testing buried pipelines rely on periodic excavation of a section of pipe and attachment of large acoustic or magneto-restrictive sensors. These systems, while reliable, suffer from a high cost-per-test ratio. Our group hopes to reduce the power constraints of such a detection system, in order to allow for permanent installations that monitor the pipelines continuously. We propose to use Time Reversal, a signal processing technique, in order to achieve this improvement. This paper will focus on the modes generated by various acoustic probing signals, and the echoes received with and without Time Reversal. We argue that TR will be most beneficial when there are several dispersive modes present, a scenario avoided in conventional techniques. We will present simulation results for the analysis of wave modes in a cylindrical pipe before and after Time Reversal using PZFlex.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The volatile nature of natural gas makes it extremely important to ensure that distribution pipelines remain free from defects, as leakage can result in explosions. Many current methods for testing buried pipelines rely on periodic excavation of a section of pipe and attachment of large acoustic or magneto-restrictive sensors. These systems, while reliable, suffer from a high cost-per-test ratio. Our group hopes to reduce the power constraints of such a detection system, in order to allow for permanent installations that monitor the pipelines continuously. We propose to use Time Reversal, a signal processing technique, in order to achieve this improvement. This paper will focus on the modes generated by various acoustic probing signals, and the echoes received with and without Time Reversal. We argue that TR will be most beneficial when there are several dispersive modes present, a scenario avoided in conventional techniques. We will present simulation results for the analysis of wave modes in a cylindrical pipe before and after Time Reversal using PZFlex. |
Nicholas O'Donoughue; Joel Harley; José M F Moura; Jin Yuanwei Detection of structural defects in pipes using time reversal of guided waves Inproceedings Proc. of the Asilomar Conference on Signals, Systems, and Computer, pp. 1683–1686, IEEE, Pacific Grove, CA, 2009. @inproceedings{ODonoughue2009-da, title = {Detection of structural defects in pipes using time reversal of guided waves}, author = {Nicholas O'Donoughue and Joel Harley and José M F Moura and Jin Yuanwei}, year = {2009}, date = {2009-01-01}, booktitle = {Proc. of the Asilomar Conference on Signals, Systems, and Computer}, pages = {1683--1686}, publisher = {IEEE}, address = {Pacific Grove, CA}, abstract = {Structural health monitoring of buried pipelines is of vital importance as infrastructures age. Ultrasonic guided waves are a popular method for inspecting buried pipes, due to their potential for long propagation. Unfortunately, the large number of wave modes present, and the effects of dispersion, in a pipeline make analysis of the received signals difficult. We plan to use Time Reversal Acoustics to compensate for these complex signals, and improve performance for the detection of faults in a pipeline. We will present theoretical performance results for conventional and Time Reversal detectors, verified with simulations conducted in PZFlex. Time Reversal shows a potential for a reduction in the power requirements of a fault detection system.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Structural health monitoring of buried pipelines is of vital importance as infrastructures age. Ultrasonic guided waves are a popular method for inspecting buried pipes, due to their potential for long propagation. Unfortunately, the large number of wave modes present, and the effects of dispersion, in a pipeline make analysis of the received signals difficult. We plan to use Time Reversal Acoustics to compensate for these complex signals, and improve performance for the detection of faults in a pipeline. We will present theoretical performance results for conventional and Time Reversal detectors, verified with simulations conducted in PZFlex. Time Reversal shows a potential for a reduction in the power requirements of a fault detection system. |
2006 |
Antonios Giannopoulos; Ansas Kasten; Christopher Long; Chen Chen; Joel Harley; Kent Choquette 2-dimensional Integrated VCSEL and PIN Photodector Arrays for Bidirectional Optical Links Inproceedings Proc. of the IEEE LEOS Annual Meeting, pp. 448–449, Montreal, QC, 2006. @inproceedings{Giannopoulos2006-fd, title = {2-dimensional Integrated VCSEL and PIN Photodector Arrays for Bidirectional Optical Links}, author = {Antonios Giannopoulos and Ansas Kasten and Christopher Long and Chen Chen and Joel Harley and Kent Choquette}, year = {2006}, date = {2006-10-01}, booktitle = {Proc. of the IEEE LEOS Annual Meeting}, pages = {448--449}, address = {Montreal, QC}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |