SmartDATA Lab Publications
2014
43.
Peng Gong, Mark Patton, D Greve, J Harley, Chang Liu, I Oppenheim
Alkali-silica reaction (ASR) detection in concrete from frequency dependent ultrasonic attenuation Journal Article
In: AIP Conference Proceedings, vol. 1581, no. 1, pp. 909–916, 2014.
@article{Gong2014-kf,
title = {Alkali-silica reaction (ASR) detection in concrete from
frequency dependent ultrasonic attenuation},
author = {Peng Gong and Mark Patton and D Greve and J Harley and Chang Liu and I Oppenheim},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&cstart=20&pagesize=80&citation_for_view=Isf8yn0AAAAJ:XvxMoLDsR5gC},
doi = {10.1063/1.4864918},
year = {2014},
date = {2014-02-01},
journal = {AIP Conference Proceedings},
volume = {1581},
number = {1},
pages = {909–916},
publisher = {American Institute of Physics},
abstract = {The alkali-silica reaction (ASR) occurs between the reactive
aggregates and the alkaline cement paste in concrete, eventually
producing damage such as swelling and cracking. This research
uses mechanical tests and ultrasonic tests to detect ASR onset in
concrete specimens. The test specimens are fabricated in pairs,
one specimen typically subjected to an accelerated ASR
environment (immersion in 1 N NaOH solution at 80°C) and the
second specimen comparable but not exposed to the accelerated ASR
environment. In mechanical tests, the transverse and longitudinal
resonant frequencies are measured. Results show that ASR damage
would lower the resonant frequencies. In the ultrasonic test,
broadband excitations are used and pitch-catch records are
obtained. The presence of ASR damage in concrete is shown to
cause frequency dependent ultrasonic attenuation. Signals from
ASR damaged specimens show strong attenuation at high frequencies
and weak attenuation at low frequencies. In contrast, signals
frompaired non-AS...},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The alkali-silica reaction (ASR) occurs between the reactive
aggregates and the alkaline cement paste in concrete, eventually
producing damage such as swelling and cracking. This research
uses mechanical tests and ultrasonic tests to detect ASR onset in
concrete specimens. The test specimens are fabricated in pairs,
one specimen typically subjected to an accelerated ASR
environment (immersion in 1 N NaOH solution at 80°C) and the
second specimen comparable but not exposed to the accelerated ASR
environment. In mechanical tests, the transverse and longitudinal
resonant frequencies are measured. Results show that ASR damage
would lower the resonant frequencies. In the ultrasonic test,
broadband excitations are used and pitch-catch records are
obtained. The presence of ASR damage in concrete is shown to
cause frequency dependent ultrasonic attenuation. Signals from
ASR damaged specimens show strong attenuation at high frequencies
and weak attenuation at low frequencies. In contrast, signals
frompaired non-AS...
aggregates and the alkaline cement paste in concrete, eventually
producing damage such as swelling and cracking. This research
uses mechanical tests and ultrasonic tests to detect ASR onset in
concrete specimens. The test specimens are fabricated in pairs,
one specimen typically subjected to an accelerated ASR
environment (immersion in 1 N NaOH solution at 80°C) and the
second specimen comparable but not exposed to the accelerated ASR
environment. In mechanical tests, the transverse and longitudinal
resonant frequencies are measured. Results show that ASR damage
would lower the resonant frequencies. In the ultrasonic test,
broadband excitations are used and pitch-catch records are
obtained. The presence of ASR damage in concrete is shown to
cause frequency dependent ultrasonic attenuation. Signals from
ASR damaged specimens show strong attenuation at high frequencies
and weak attenuation at low frequencies. In contrast, signals
frompaired non-AS...
42.
Joel B Harley, Jose M F Moura
Temperature Compensation in Wave-Based Damage Detection Systems Miscellaneous
2014.
@misc{Harley2014-ys,
title = {Temperature Compensation in Wave-Based Damage Detection Systems},
author = {Joel B Harley and Jose M F Moura},
url = {https://patentimages.storage.googleapis.com/fb/a8/89/972f0458a07086/US20140025316A1.pdf},
year = {2014},
date = {2014-01-01},
journal = {United States Patent and Trademark Office},
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 = {misc}
}
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.
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
41.
Yujie Ying, James H Jr 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
In: Journal of Computing in Civil Engineering, vol. 27, no. 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 Jr Garrett and Irving J Oppenheim and Lucio Soibelman and Joel B Harley and Jun Shi and Yuanwei Jin},
url = {https://ascelibrary.org/doi/abs/10.1061/(ASCE)CP.1943-5487.0000258?casa_token=JBRpvkihc2IAAAAA:i4HD7iIh2z2mJjVwJWeQqahTggGXIT7dfC6e0oSzHWW4gxRHUBK-2QSFun9wLwytLEeUpqFW},
doi = {10.1061/(asce)cp.1943-5487.0000258},
year = {2013},
date = {2013-11-01},
journal = {Journal of Computing in Civil Engineering},
volume = {27},
number = {6},
pages = {667–680},
publisher = {American Society of Civil Engineers (ASCE)},
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 …},
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 …
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 …
40.
Joel B Harley, Chang Liu, Irving J Oppenheim, José M F Moura
High resolution localization with Lamb wave sparse wavenumber analysis Proceedings Article
In: Chang, Fu-Kuo (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},
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.
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.
39.
J B Harley, J M F Moura
Decomposition of multipath Lamb waves with sparse wavenumber analysis for structural health monitoring Proceedings Article
In: 2013 IEEE International Ultrasonics Symposium (IUS), pp. 675–678, IEEE, Prague, 2013.
@inproceedings{Harley2013-om,
title = {Decomposition of multipath Lamb waves with sparse wavenumber
analysis for structural health monitoring},
author = {J B Harley and J M F Moura},
url = {http://dx.doi.org/10.1109/ULTSYM.2013.0174},
doi = {10.1109/ULTSYM.2013.0174},
year = {2013},
date = {2013-07-01},
booktitle = {2013 IEEE International Ultrasonics Symposium (IUS)},
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
techniques. 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
techniques. Its application to experimental Lamb wave data shows
that the estimates all correspond to expected paths.
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
techniques. Its application to experimental Lamb wave data shows
that the estimates all correspond to expected paths.
38.
S Chen, F Cerda, J Guo, J B Harley, Q Shi, P Rizzo, J Bielak, J H Garrett, J Kovačević
Multiresolution classification with semi-supervised learning for indirect bridge structural health monitoring Proceedings Article
In: 2013 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 = {S Chen and F Cerda and J Guo and J B Harley and Q Shi and P Rizzo and J Bielak and J H Garrett and J Kovačević},
url = {http://dx.doi.org/10.1109/ICASSP.2013.6638291},
doi = {10.1109/ICASSP.2013.6638291},
year = {2013},
date = {2013-05-01},
booktitle = {2013 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
monitoring of bridges. The monitoring approach envisions a
sensing 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-supervised
weighting algorithm within a multiresolution classification
framework. We show that the proposed algorithm performs
significantly better than supervised multiresolution
classification.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a multiresolution classification framework with
semi-supervised learning for the indirect structural health
monitoring of bridges. The monitoring approach envisions a
sensing 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-supervised
weighting algorithm within a multiresolution classification
framework. We show that the proposed algorithm performs
significantly better than supervised multiresolution
classification.
semi-supervised learning for the indirect structural health
monitoring of bridges. The monitoring approach envisions a
sensing 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-supervised
weighting algorithm within a multiresolution classification
framework. We show that the proposed algorithm performs
significantly better than supervised multiresolution
classification.
37.
Joel B Harley, Jose M F Moura
Broadband localization in a dispersive medium through sparse wavenumber analysis Proceedings Article
In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, 2013.
@inproceedings{Harley2013-nj,
title = {Broadband localization in a dispersive medium through sparse
wavenumber analysis},
author = {Joel B Harley and Jose M F Moura},
url = {https://ieeexplore.ieee.org/abstract/document/6638424/},
doi = {10.1109/icassp.2013.6638424},
year = {2013},
date = {2013-05-01},
booktitle = {2013 IEEE International Conference on Acoustics, Speech and
Signal Processing},
publisher = {IEEE},
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 …},
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 …
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 …
36.
Chang Liu, Joel B Harley, Yujie Ying, Irving J Oppenheim, Mario Bergés, David W Greve, James H Jr Garrett
Singular value decomposition for novelty detection in ultrasonic pipe monitoring Proceedings Article
In: Lynch, Jerome P; Yun, Chung-Bang; Wang, Kon-Well (Ed.): Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013, pp. 86921R, International Society for Optics and Photonics, 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 Jr Garrett},
editor = {Jerome P Lynch and Chung-Bang Yun and Kon-Well Wang},
url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2009891},
doi = {10.1117/12.2009891},
year = {2013},
date = {2013-04-01},
booktitle = {Sensors and Smart Structures Technologies for Civil, Mechanical,
and Aerospace Systems 2013},
volume = {8692},
pages = {86921R},
publisher = {International Society for Optics and Photonics},
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.
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.
35.
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 Proceedings Article
In: Lynch, Jerome P; Yun, Chung-Bang; Wang, Kon-Well (Ed.): Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013, pp. 498–508, SPIE, 2013.
@inproceedings{Liu2013-re,
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 = {http://dx.doi.org/10.1117/12.2009891},
doi = {10.1117/12.2009891},
year = {2013},
date = {2013-04-01},
booktitle = {Sensors and Smart Structures Technologies for Civil, Mechanical,
and Aerospace Systems 2013},
volume = {8692},
pages = {498–508},
publisher = {SPIE},
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.
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.
34.
Liu Chang, Harley Joel B., Ying Yujie, Altschul Martin H., Bergés Mario, Jr James H Garrett, Greve David W., Moura José M. F., Oppenheim Irving J., Soibelman Lucio
Ultrasonic Monitoring of a Pressurized Pipe in Operation Proceedings Article
In: Structures Congress 2013, pp. 1903–1913, American Society of Civil Engineers, Reston, VA, 2013.
@inproceedings{Liu-Chang2013-yy,
title = {Ultrasonic Monitoring of a Pressurized Pipe in Operation},
author = {Liu Chang and Harley Joel B. and Ying Yujie and Altschul Martin H. and Bergés Mario and Jr James H Garrett and Greve David W. and Moura José M. F. and Oppenheim Irving J. and Soibelman Lucio},
url = {http://ascelibrary.org/doi/10.1061/9780784412848.167},
doi = {10.1061/9780784412848.167},
year = {2013},
date = {2013-04-01},
booktitle = {Structures Congress 2013},
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
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
33.
Chang Liu, Joel B Harley, Yujie Ying, Martin H Altschul, Mario Bergés, James H Jr Garrett, David W Greve, José M F Moura, Irving J Oppenheim, Lucio Soibelman
Ultrasonic monitoring of a pressurized pipe in operation Proceedings Article
In: Structures Congress 2013, pp. 1903–1913, American Society of Civil Engineers, Reston, VA, 2013.
@inproceedings{Liu2013-gw,
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 Jr Garrett and David W Greve and José M F Moura and Irving J Oppenheim and Lucio Soibelman},
url = {http://dx.doi.org/10.1061/9780784412848.167},
doi = {10.1061/9780784412848.167},
year = {2013},
date = {2013-04-01},
booktitle = {Structures Congress 2013},
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.},
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.
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.
32.
Yujie Ying, James H Jr. 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
In: Journal of Pipeline Systems Engineering and Practice, vol. 4, no. 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 Jr. Garrett and Joel Harley and Irving J Oppenheim and Jun Shi and Lucio Soibelman},
url = {http://ascelibrary.org/doi/10.1061/%28ASCE%29PS.1949-1204.0000106},
doi = {10.1061/(ASCE)PS.1949-1204.0000106},
year = {2013},
date = {2013-02-01},
journal = {Journal of Pipeline Systems Engineering and Practice},
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.
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.
31.
Joel B Harley, Nattamon Thavornpitak, José M F Moura
Delay-and-sum technique for localization of active sources in cylindrical objects Journal Article
In: AIP Conference Proceedings, vol. 1511, no. 1, pp. 294–301, 2013.
@article{Harley2013-or,
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://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&cstart=100&pagesize=100&citation_for_view=Isf8yn0AAAAJ:MXK_kJrjxJIC},
doi = {10.1063/1.4789061},
year = {2013},
date = {2013-01-01},
journal = {AIP Conference Proceedings},
volume = {1511},
number = {1},
pages = {294–301},
publisher = {American Institute of Physics},
address = {Denver, CO},
series = {AIP Conference Proceedings},
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 = {article}
}
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.
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.
30.
Chang Liu, Joel B Harley, Nicholas O'Donoughue, Yujie Ying, Mario Berges, Martin H Altschul, James H Jr Garrett, David Greve, José M F Moura, Irving J Oppenheim, Lucio Soibelman
Ultrasonic scatterer detection in a pipe under operating conditions using singular value decomposition Journal Article
In: AIP Conference Proceedings, vol. 1511, no. 1, pp. 1454–1461, 2013.
@article{Liu2013-zx,
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 Berges and Martin H Altschul and James H Jr Garrett and David Greve and José M F Moura and Irving J Oppenheim and Lucio Soibelman},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&cstart=20&pagesize=80&citation_for_view=Isf8yn0AAAAJ:3fE2CSJIrl8C},
doi = {10.1063/1.4789213},
year = {2013},
date = {2013-01-01},
journal = {AIP Conference Proceedings},
volume = {1511},
number = {1},
pages = {1454–1461},
publisher = {American Institute of Physics},
address = {Denver, CO},
series = {AIP Conference Proceedings},
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 = {article}
}
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.
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.
2012
29.
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 Proceedings Article
In: 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pp. 1863–1867, IEEE, 2012.
@inproceedings{O-Donoughue2012-dy,
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 = {http://dx.doi.org/10.1109/acssc.2012.6489360},
doi = {10.1109/acssc.2012.6489360},
year = {2012},
date = {2012-11-01},
booktitle = {2012 Conference Record of the Forty Sixth Asilomar Conference on
Signals, Systems and Computers (ASILOMAR)},
pages = {1863–1867},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
28.
Chang Liu, Joel Harley, Nicholas O'Donoughue, Yujie Ying, Martin H Altschul, Mario Berges, 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 Proceedings Article
In: 2012 IEEE International Ultrasonics Symposium, pp. 483–486, IEEE, Dresden, 2012.
@inproceedings{Liu2012-jq,
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 Berges and James H Garrett and David W Greve and Jose M F Moura and Irving J Oppenheim and Lucio Soibelman},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&cstart=20&pagesize=80&citation_for_view=Isf8yn0AAAAJ:M3ejUd6NZC8C},
doi = {10.1109/ultsym.2012.0120},
year = {2012},
date = {2012-10-01},
booktitle = {2012 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.
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.
27.
Joel B Harley, Aurora C Schmidt, Jose M F Moura
Accurate sparse recovery of guided wave characteristics for structural health monitoring Proceedings Article
In: 2012 IEEE International Ultrasonics Symposium, pp. 158–161, IEEE, 2012.
@inproceedings{Harley2012-ew,
title = {Accurate sparse recovery of guided wave characteristics for
structural health monitoring},
author = {Joel B Harley and Aurora C Schmidt and Jose M F Moura},
url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Isf8yn0AAAAJ&cstart=20&pagesize=80&citation_for_view=Isf8yn0AAAAJ:4TOpqqG69KYC},
doi = {10.1109/ultsym.2012.0039},
year = {2012},
date = {2012-10-01},
booktitle = {2012 IEEE International Ultrasonics Symposium},
pages = {158–161},
publisher = {IEEE},
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 ℓ 1
-regularized optimization techniques to recover the sparse
characteristics of the guided wavefields in the
frequency-wavenumber 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 …},
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 ℓ 1
-regularized optimization techniques to recover the sparse
characteristics of the guided wavefields in the
frequency-wavenumber 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 …
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 ℓ 1
-regularized optimization techniques to recover the sparse
characteristics of the guided wavefields in the
frequency-wavenumber 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 …
26.
Joel B Harley, José M F Moura
Scale transform signal processing for optimal ultrasonic temperature compensation Journal Article
In: IEEE transactions on ultrasonics, ferroelectrics, and frequency control, vol. 59, no. 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 = {http://dx.doi.org/10.1109/TUFFC.2012.2448},
doi = {10.1109/TUFFC.2012.2448},
year = {2012},
date = {2012-10-01},
journal = {IEEE transactions on ultrasonics, ferroelectrics, and frequency
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.
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.
25.
A Schmidt, J B Harley, J M F Moura
Compressed sensing radar surveillance networks Proceedings Article
In: 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 209–212, IEEE, Hoboken, NJ, 2012.
@inproceedings{Schmidt2012-pa,
title = {Compressed sensing radar surveillance networks},
author = {A Schmidt and J B Harley and J M F Moura},
url = {http://dx.doi.org/10.1109/SAM.2012.6250469},
doi = {10.1109/SAM.2012.6250469},
year = {2012},
date = {2012-06-01},
booktitle = {2012 IEEE 7th Sensor Array and Multichannel Signal Processing
Workshop (SAM)},
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.
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.
24.
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 Journal Article
In: AIP conference proceedings, vol. 1430, no. 1, pp. 1551–1558, 2012.
@article{Harley2012-be,
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://aip.scitation.org/doi/abs/10.1063/1.4716399},
doi = {10.1063/1.4716399},
year = {2012},
date = {2012-05-01},
journal = {AIP conference proceedings},
volume = {1430},
number = {1},
pages = {1551–1558},
publisher = {American Institute of Physics},
address = {Burlington, VT},
series = {AIP Conference Proceedings},
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 the
Mellin transform for 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 = {article}
}
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 the
Mellin transform for 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.
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 the
Mellin transform for 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.