Code and Resources

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 code, 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 the dynamic time warping is more robust to the above-mentioned errors and more accurately detects damage with weak ultrasonic signatures.

This code 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.

This code demonstrates a computationally efficient algebraic graph theory engine for simulating time-domain one-dimensional waves in a multi-segment transmission line, such as for reflectometry applications. Efficient simulation of time-domain signals in multi-segment transmission lines is challenging because the number of propagation paths (and therefore the number of operations) increases exponentially with each new interface. We address this challenge through the use of a frequency-domain, algebraic graphical model of wave propagation, which is then converted to the time domain via the Fourier transform. We use this model to achieve an exact, stable, and computationally efficient [O(NQ), where N is the number of segments and Q is the bandwidth] approach for studying one-dimensional wave propagation. Our approach requires the reflection and transmission coefficients for each interface and each segment’s complex propagation constant. We compare our simulation results with known analytical solutions.

Guided wave methodologies are among the established approaches for structural health monitoring (SHM). 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 multimodal 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 (SWA), 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 SWA, for predicting the behavior of guided waves in composite materials. We implement these techniques on several experimental and simulation data sets. 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.