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While guided wave structural health monitoring (SHM) is widely researched for ensuring safety, estimating performance deterioration, and detecting damage in structures, it experiences setbacks in accuracy due to varying environmental, sensor, and material factors. To combat these challenges, environmentally variable guided wave data is often stretched with temperature compensation methods, such as the scale transform and optimal signal stretch, to match a baseline signal and enable accurate damage detection. Yet, these methods fail for large environmental changes. This paper addresses this challenge by demonstrating a machine learning method to predict stretch factors. This is accomplished with feed-forward neural networks that approximate the complex velocity change function. We demonstrate that our machine learning approach outperforms the prior art on simulated Lamb wave data and is robust with extreme velocity variations. While our machine learning models do not conduct temperature compensation, their accurate stretch factor predictions serve as a proof of concept that a better model is plausible.

 

This paper studies the effectiveness of joint compression and denoising strategies with realistic, long-term guided wave structural health monitoring data. We leverage the high correlation between nearby collections of guided waves in time to create sparse and low-rank representations. While compression and denoising schemes are not new, they are almost exclusively designed and studied with relatively simple datasets. In contrast, guided wave structural health monitoring datasets have much more complex operational and environmental conditions, such as temperature, that distort data and for which the requirements to achieve effective compression and denoising are not well understood. The paper studies how to optimize our data collection and algorithms to best utilize guided wave data for compression, denoising, and damage detection based on seven million guided wave measurements collected over 2 years.

 

Modern machine learning has been on the rise in many scientific domains, such as acoustics. Many scientific problems face challenges with limited data, which prevent the use of the many powerful machine learning strategies. In response, the physics of wave-propagation can be exploited to reduce the amount of data necessary and improve performance of machine learning techniques. Based on this need, we present a physics-informed machine learning framework, known as wave-informed regression, to extract dispersion curves from a guided wave wavefield data from non-homogeneous media. Wave-informed regression blends matrix factorization with known wave-physics by borrowing results from optimization theory. We briefly derive the algorithm and discuss a signal processing-based interpretability aspect of it, which aids in extracting dispersion curves for non-homogenous media. We show our results on a non-homogeneous media, where the dispersion curves change as a function of space. We demonstrate our ability to use wave-informed regression to extract spatially local dispersion curves. 

Guided wave testing is a popular approach for monitoring the structural integrity of infrastructures. We focus on the primary task of damage detection, where signal processing techniques are commonly employed. The detection performance is affected by a mismatch between the wave propagation model and experimental wave data. External variations, such as temperature, which are difficult to model, also affect the performance. While deep learning models can be an alternative detection method, there is often a lack of real-world training datasets. In this work, we counter this challenge by training an ensemble of variational autoencoders only on simulation data with a wave physics-guided adversarial component. We set up an experiment with non-uniform temperature variations to test the robustness of the methods. We compare our scheme with existing deep learning detection schemes and observe superior performance on experimental data. 

Guided ultrasonic wave localization systems use spatially distributed sensor arrays and wave propagation models to detect and locate damage across a structure. Environmental and operational conditions, such as temperature or stress variations, introduce uncertainty into guided wave data and reduce the effectiveness of these localization systems. These uncertainties cause the models used by each localization algorithm to fail to match with reality. This paper addresses this challenge with an ensemble deep neural network that is trained solely with simulated data. Relative to delay-and-sum and matched field processing strategies, this approach is demonstrated to be more robust to temperature variations in experimental data. As a result, this approach demonstrates superior accuracy with small numbers of sensors and greater resilience to spatially nonhomogeneous temperature variations over time.

 

Environmental effects are a significant challenge in guided wave structural health monitoring systems. These effects distort signals and increase the likelihood of false alarms. Many research papers have studied mitigation strategies for common variations in guided wave datasets reproducible in a lab, such as temperature and stress. There are fewer studies and strategies for detecting damage under more unpredictable outdoor conditions. This article proposes a long short-term principal component analysis reconstruction method to detect synthetic damage under highly variational environments, like precipitation, freeze, and other conditions. The method does not require any temperature or other compensation methods and is tested by approximately seven million guided wave measurements collected over 2 years. Results show that our method achieves an area under curve score of near 0.95 when detecting synthetic damage under highly variable environmental conditions. 

Detecting and locating damage information from waves reflected off damage is a common practice in non-destructive structural health monitoring systems. Yet, the transmitted ultrasonic guided waves are affected by the physical and material properties of the structure and are often complicated to model mathematically. This calls for data-driven approaches to model the behaviour of waves, where patterns in wave data due to damage can be learned and distinguished from non-damage data. Recent works have used a popular dictionary learning algorithm, K-SVD, to learn an overcomplete dictionary for waves propagating in a metal plate. However, the domain knowledge is not utilized. This may lead to fruitless results in the case where there are strong patterns in the data that are not of interest to the domain. In this work, instead of treating the K-SVD algorithm as a black box, we create a novel modification by enforcing domain knowledge. In particular, we look at how regularizing the K-SVD algorithm with the one-dimensional wave equation affects the dictionary learned in the simple case of vibrating string. By adding additional non-wave patterns (noise) to the data, we demonstrate that the “wave-informed K-SVD” does not learn patterns which do not obey the wave equation hence learning patterns from data and not the noise. 

Damage detection and localization remain challenging research areas in structural health monitoring. Guided wave-based methods that utilize signal processing tools (e.g., matched field processing and delay-and-sum localization) have enjoyed success in damage detection. To locate damage, such techniques rely on a model of wave propagation through materials. Measured data is then compared with these models to determine the origin of a wave. As a result, the analytical model and actual data may have a mismatch due to environmental variations or a lack of knowledge about the material. Deep neural networks are a class of machine learning algorithms that learn a non-linear functional mapping. The paper presents a deep neural network-based approach to damage localization. We use simulated data to assess the performance of localization frameworks under varying levels of noise and other uncertainty in our ultrasonic signals. 

This work discusses an optimization framework to embed dictionary learning frameworks with the wave equation as a strategy for incorporating prior scientific knowledge into a machine learning algorithm. We modify dictionary learning to study ultrasonic guided wave-based defect detection for non-destructive structural health monitoring systems. Specifically, this work involves altering the popular-SVD algorithm for dictionary learning by enforcing prior knowledge about the ultrasonic guided wave problem through a physics-based regularization derived from the wave equation. We confer it the name “wave-informed K-SVD.” Training dictionary on data simulated from a fixed string added with noise using both K-SVD and wave-informed K-SVD, we show an improved physical consistency of columns of dictionary matrix with the known modal behavior of different one-dimensional wave simulations is observed.