Guided Wave Structural Health Monitoring Research Articles

Research on detection of switch rail defects based on pulse reflection method of the dominant mode

Guided wave structural health monitoring offers a promising way for continuous surveillance and identification of structural damage. However, due to the variable cross-section properties of the switch rail in the longitudinal direction, there are different modes present on different cross-sections, so the guided wave signals excited are very complex. The traditional baseline subtraction method can be used to extract defect echo features, but it is prone to false positives due to the influence of external factors such as field temperature change and environmental noise. This paper proposes a switch rail defect detection method based on the pulse reflection method of the dominant mode. Initially, eight typical cross-sections are chosen based on the cross-sectional dimensions of the switch rail. Semi-analytical finite element(SAFE) method is utilized to solve the dispersion curves and wave structure matrix of guided waves in the switch rail. According to the 3σ rule, the dominant mode whose node amplitude is much higher than other modes is selected, and the excitation signal is applied at the maximum value of the mode, which successfully excites the dominant mode. Through simulation and experimental validation, the reflected waves of the dominant mode at the defect are observable and visible, and the pulse reflection method can be used to detect the defects at the switch rail foot with a positioning error of less than 10 cm. The pulse reflection method of the dominant mode proposed in this paper, which has high detection efficiency and is not affected by the ambient temperature, provides a new idea and way to detect defects of the switch rail based on the ultrasonic guided wave method.

Defect detection and localisation using guided wave images from array data processed by nonlinear autoregressive exogenous model and Gamma statistical operator

Guided wave structural health monitoring (GWSHM) systems, using the delay-and-sum imaging algorithm, are an efficient solution to detect and localise defects in industrial structures. However, the image artifacts caused by either imperfect detection or sensor lay-out limitations make it difficult to identify and locate defects accurately. In order to enhance the performance of defect detection and localisation in GWSHM systems, this paper proposes a three-step procedure for post-processing guided wave signals prior to image formation. In the first step, the signals are processed using the nonlinear autoregressive exogenous model to suppress noise from benign features. The second step calculates the probability of defect presence based on the rescaled Gamma cumulative distribution function. This probabilistic threshold is then determined from the quantile mapping. Finally, a guide wave image is formed using the delay-and-sum imaging algorithm. The experimental validation was performed to inspect a 6 mm-diameter through-thickness circular hole on an aluminium plate and the defects were further scaled as simulated datasets to test its detectability under various amplitudes. In the second procedure step, the detection and localisation performance of the proposed procedure was compared with that of using the signal difference coefficient and the Rayleigh maximum likelihood estimator. It is shown that the proposed procedure can enhance the contrast between damaged and undamaged regions, providing more reliable and accurate guided wave images.

Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device

Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units, various deep learning based models have been proposed for improving performance of ultrasonic guided wave structural health monitoring (GW-SHM) systems, especially to counter complexity and heterogeneity in data due to varying environmental factors (e.g., temperature) and types of damages. Such models typically comprise of millions of trainable parameters, and therefore add to cost of deployment due to requirements of cloud connectivity and processing, thus limiting the scale of deployment of GW-SHM. In this work, we propose an alternative solution that leverages TinyML framework for development of light-weight ML models that could be directly deployed on embedded edge devices. The utility of our solution is illustrated by presenting an unsupervised learning framework for damage detection in honeycomb composite sandwich structure with disbond and delamination type of damages, validated using data generated by finite element simulations and experiments performed at various temperatures in the range 0–90 °C. We demonstrate a fully-integrated solution using a Xilinx Artix-7 FPGA for data acquisition and control, and edge-inference of damage. Despite the limited number of features, the lightweight model shows reasonably high accuracy, thereby enabling detection of small size defects with improved sensitivity on an edge device for online GW-SHM.

A multi-layer ML model evolutionary paradigm for high-accuracy individual aircraft SHM

Evolutionary Machine Learning (EML) has emerged as a novel direction for providing robust methodologies for effectively tackling complex problems. However, most current research focuses on multi-objective optimization problems in machine learning (ML). There is limited research on the EML mechanism along the temporal application process, where a fixed machine learning model trained with the prior dataset will inevitably be not representative of the target problem with time. Taking the Structural Health Monitoring (SHM)-ML damage diagnosis model as an example, this paper proposes a novel multi-layer ML model evolutionary paradigm with multi-source data for high-accuracy individual aircraft SHM. The proposed paradigm considers three interconnected layers for the SHM-ML damage diagnosis model, including the dataset to establish the diagnosis model, the model structure and its parameter training, and mapping relationship used in the model to give the damage size. Besides, for a specific model of aircraft, there exist multiple stages from design to service, where multi-source of data is fused for the multi-layer evolution, enabling continuous improvement of damage diagnosis accuracy. Experimental validation is conducted on critical connection structures of aircraft, specifically the attachment lug structure, using the active guided wave SHM method. The results demonstrate the effectiveness of the proposed multi-layer ML model evolutionary paradigm.

Time–Frequency Analysis Using Stockwell Transform with Application to Guided Wave Structural Health Monitoring

Time–Frequency Analysis Using Stockwell Transform with Application to Guided Wave Structural Health Monitoring

Study of Ultrasonic Guided Wave Propagation in Bone Composite Structures for Revealing Osteoporosis Diagnostic Indicators.

Tubular bones are layered waveguide structures composed of soft tissue, cortical and porous bone tissue, and bone marrow. Ultrasound diagnostics of such biocomposites are based on the guided wave excitation and registration by piezoelectric transducers applied to the waveguide surface. Meanwhile, the upper sublayers shield the diseased interior, creating difficulties in extracting information about its weakening from the surface signals. To overcome these difficulties, we exploit the advantages of the Green's matrix-based approach and adopt the methods and algorithms developed for the guided wave structural health monitoring of industrial composites. Based on the computer models implementing this approach and experimental measurements performed on bone phantoms, we analyze the feasibility of using different wave characteristics to detect hidden diagnostic signs of developing osteoporosis. It is shown that, despite the poor excitability of the most useful modes associated with the diseased inner layers, the use of the improved matrix pencil method combined with objective functions based on the Green's matrix allows for effective monitoring of changes in the elastic moduli of the deeper sublayers. We also note the sensitivity and monotonic dependence of the resonance response frequencies on the degradation of elastic properties, making them a promising indicator for osteoporosis diagnostics.

Development of an ultrasonic wave measurement device based on Fiber Bragg Gratings in optical fibers: application to Guided Wave Structural Health Monitoring

Optical fiber transducers such as Fiber Bragg Gratings (FBG) are promising solutions for guided waves Structural Health Monitoring (SHM) due to their low intrusivity and their ability to cope with harsh environments (extreme temperatures, radiations, explosive atmospheres…). We present here a solution enabling measurements under varying environmental and operational conditions, which is mandatory for many applications in SHM, the low complexity of the solution ensuring good integration capacities. The effect of the length of the FBG was also studied to enable the optimization of the system sensitivity.

Combined two-level guided wave structural health monitoring strategy using multifeature integration and machine learning: application to early-age grouted sleeves

Structural health monitoring of grouted sleeves is one of the assembly industry’s huge challenges. In this study, a combined two-level damage detection was introduced. It comprises defect classification (healthy, rebar eccentricity, and grout leakage) and severity evaluation for early-age grouted sleeves using guided waves. Multiple features (MF) from time-, frequency-, and time-frequency domains were extracted and defined according to the diverse defects and ages of grouted sleeves to represent complex damage characteristics. Moreover, the egret swarm algorithm optimization–extreme learning machine (ESAO-ELM) models were proposed to avoid the influence of subjective experience and judgment from experts. ESAO optimized the initial random parameters (input weights and hidden layer bias) of ELM. Then, two MF-ESAO-ELM models were trained for two-level damage detection on the experimental dataset. The performance of the proposed models was comprehensively evaluated using accuracy, recall, precision, and confusion matrix. MF-ESAO-ELM performs better than ELM and PSO-ELM in accuracy. In this strategy, the defect classification model works in the outer layer to distinguish the state and types of defects of grouted sleeves (healthy, eccentric, or leakage). In comparison, the inner layer starts predicting the severity only if the defect type is leakage. MF-ESAO-ELM offers advantages in terms of accuracy, strategy, and calculation time.

Unsupervised long-term damage detection in an uncontrolled environment through optimal autoencoder

Unsupervised damage detection in the presence of both regular environmental variations, such as a daily change in temperature and humidity, and irregular environmental variations, such as rain and snow, remains a critical challenge in guided wave structural health monitoring. This paper proposes an optimal autoencoder-based damage detection strategy to solve this problem. Although the autoencoder and other neural networks have been used to detect anomalies in structural health monitoring, the training data has been also required to be collected from a known intact structure, which increases the time and labor cost for inspection and limits the application of such methods. Instead, our autoencoder is trained with guided wave data that contain uncontrolled regular, irregular, and damage variations. We investigate hyperparameters that will negatively influence autoencoder-based damage detection, including the training time and damage duration. We also propose sparsity, dropout, and weight decay regularization strategies to enhance the robustness of the autoencoder to these hyperparameters. Results show that our optimal autoencoder method can achieve an area under the receiver operating curve score near 0.92 for detecting damage present in the last 16 days, which improves previous local principal components analysis reconstruction methods with a score of 0.88.

Sparse sampled visualization of ultrasonic guided waves for defect identification in plate structures

Ultrasonic wave visualization is an effective tool to identify defects along with their location and topology in guided wave structural health monitoring applications. However, conventional imaging techniques require relatively longer data acquisition time and large storage space. Reconstructing waves from sparse sampled images can potentially overcome such difficulties. In this regard, sparse sampling techniques based on random sampling and uniform sampling are explored in this paper. Specifically random sparse sampling using Poisson disk with biharmonic interpolation (PDS-BI) technique is shown to achieve defect identification with an image sparsity of 95% and a sensor density of 5λ2. On the other hand, a novel technique based on uniform sparse sampling with carrier multiplication (US-CM) is demonstrated to reconstruct the image with a sparsity of greater than 98% and a sensor density less than 2λ2. The performance of the proposed techniques has been quantified using Structural Similarity (SSIM) metric and validated through experiments. The reconstructed images are found to be in good agreement with those obtained by a dense array with much larger number of receivers. We also experimentally demonstrate the visualization of ultrasonic wave propagation in a metallic plate using a surface-bonded fiber Bragg grating sensor as an alternative to the conventional sensor.

Damage detection in guided wave structural health monitoring using Gaussian process regression

Damage detection using permanently installed guided wave sensors typically involves identifying changes in A-scan data from a nominally defect-free state. A well-recognised challenge is the influence of changing operational and environmental conditions which can lead to complex evolution of the A-scan data which may mask the presence of damage. This paper is based on the observation that the amplitude at each location within the A-scan has a unique functional dependence with temperature and a unique associated uncertainty in the functional dependence. The uncertainty in the functional dependence is based on both the repeatability of the measurements and the amount of data available for characterisation. The fact that each location in an A-scan has a different uncertainty leads to the concept of an adaptive threshold. In contrast to typical damage detection that utilises a flat threshold for the whole A-scan, an adaptive threshold will be lower in locations of greater predictability and rise in locations of poorer repeatability. To achieve the aim of an adaptive threshold, the amplitude–temperature dependence and associated uncertainty is characterised for each data-point in an A-scan based on a training set of defect-free data. The inverse process can then be performed to generate a new reference A-Scan with associated confidence bounds for a given temperature. The confidence bounds act as the adaptive threshold; if a new measurement passes outside the confidence bounds then it indicates statistically significant difference between the new measurement and the training set. To demonstrate the method, pitch-catch data between two sensors of a pipe-bend over 5 months and a temperature range of approximately 50°C is used. To demonstrate the damage detection capability in a controlled way synthetic damage is added to the A-scans to produce test data. The performance is compared to Optimal baseline subtraction (OBS). In this example, the performance of the Gaussian process regression adaptive threshold method exceeds OBS when the training set exceeds around 40 measurements.

Guidelines for effective unsupervised guided wave compression and denoising in long-term guided wave structural health monitoring

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.

Sliding Window Dynamic Time-Series Warping-Based Ultrasonic Guided Wave Temperature Compensation and Defect Monitoring Method for Turnout Rail Foot.

Temperature changes are a major challenge in outdoor guided wave structural health monitoring of rails. Temperature variations greatly impact the waveform of guided wave signals, making it challenging to diagnose and characterize defects. Traditional temperature compensation methods, such as signal stretch and scale transform, are restricted to use in regular structures, such as plates and pipes. To solve the temperature compensation problem in long rails with serious mode conversion and complex structure echo, we propose a temperature compensation and defect monitoring method, namely, sliding window dynamic time-series warping (SWDTW), which overcomes the challenges of mass computation and overcompensation of dynamic time-series warping (DTW). The basic idea of SWDTW is to utilize sliding windows to accelerate the computation and identify defects from subsequence scales. Then, an index, window subsequence Teager energy (WSTE), is used to indicate the local abnormality of guided wave signals, and a sliding window net (SWnet) is devised to monitor the occurrence of defects automatically. Outdoor monitoring of turnout rails showed that the proposed method can effectively reduce the temperature noise and recognize an artificial defect with 1.16% and 0.36% cross-sectional change rates (CSCRs) on the switch and stock rails, respectively, at different temperatures; moreover, the defect signals processed by SWDTW showed better defect identification performance than those processed by scale transform and DTW.

Experiments and modelling of ultrasonic waves in composite plates under varying temperature

Guided wave (GW) structural health monitoring (SHM) systems offer an attractive solution as an in-situ quasi real-time assessment of structural damage, but their sensitivity and efficiency may be impaired under varied environmental and operational conditions. Thus, virtual tests, such as that based on the Finite Element (FE) method, represent a valid approach for simulating and investigating SHM systems, enabling a substantial reduction in experimental campaigns. In this work, GW propagation characteristics in a carbon fibre-reinforced composite plate are studied under a varying temperature condition, representative of the aeronautics application. At first, GW SHM system was physically tested at room temperature (20°C), and the results were used to calibrate and assess the proposed FE modelling approaches, characterised by different element types and mesh sizes. A temperature independent averaged time compensation factor is proposed to mitigate the numerical data dependency on excitation frequency and propagation angle. Two temperature variations (from 20°C to -50°C, and 20°C to 65°C) were experimentally and numerically considered to investigate the effect of varying temperature on the GW. For all test cases, the compensated numerical data was compared to the experimental results, and discussed in terms of dispersion curves, focusing on the zero-order symmetric, S0, and antisymmetric, A0, modes. Results show that both 2D and 3D FE approaches can accurately predict the changes in GW due to varying temperature, with the group velocity of the A0 mode being less sensitive to temperature variations.

Assessment of Damage in Composite Pressure Vessels Using Guided Waves.

This paper deals with guided wave-based structural health monitoring of composite overwrapped pressure vessels adopted for space application. Indeed, they are well suited for this scope thanks to their improved performance compared with metallic tanks. However, they are characterized by a complex damage mechanics and suffer from impact induced damage, e.g., due to space debris. After reviewing the limited progress in this specific application, the paper thoroughly covers all the steps needed to design and verify guided wave structural health monitoring system, including methodology, digital modelling, reliability, and noise estimation for a correct decision-making process in a virtual environment. In particular, propagation characteristics of the fundamental anti-symmetric mode are derived experimentally on a real specimen to validate a variety of finite element models useful to investigate wave interaction with damage. Different signal processing techniques are demonstrated sensitive to defect and linearly dependent upon damage severity, showing promising reliability. Those features can be implemented in a probability-based diagnostic imaging in order to detect and localized impact induce damage. A multi-parameter approach is achieved by metrics fusion demonstrating increased capability in damage detection with promising implication in enhancing probability of detection.

Enhancing damage localisation in critical areas of storage tanks in floating production storage and offloadings with a guided wave structural health monitoring system

A guided wave monitoring system for storage tanks of floating production storage and offloading units was tested on a panel which is representative of a critical region of such structures. Initially, baseline signals of the undamaged structure were acquired within a representative temperature range. These signals were then processed with the optimal baseline subtraction, optimal stretch and phase-shift compensation algorithms. Two distinct damage indexes were generated from the residual signals in order to verify the detection capability of the system. A delay-and-sum image algorithm was then applied to the residual signals in order to evaluate the defect localisation capabilities of the system. These images were than post-processed with three different methods, one based on a probabilistic approach, another on singular value decomposition and also with a combination of these two methods. The signal-to-noise ratio (SNR) gain obtained by applying the post-processing strategies was calculated and compared to the original images. Results demonstrate that the system is capable of detecting localised wall loss and indicating its correct position for damage larger than 20% wall thickness loss. Results also show that the post-processing methods helped to improve the SNR, leading to gains of more than 15dB in damage to artefact amplitude ratios.

An up-scaling temperature compensation framework for guided wave–based structural health monitoring in large composite structures

Variations in environmental conditions can significantly impair the accuracy and reliability of guided wave structural health monitoring systems. Acquisition of baseline signals over a wide temperature range for the purposes of damage detection and localization is impractical for large composite structures. A novel framework for compensating the effect of temperature at a post-processing stage is presented in this paper to allow updating the compensation factors using observations obtained at different scales. The proposed methodology utilizes observations collected at the lower scales, where a large amount of data under controlled environment is available. Subsequently, the estimated compensation factors are propagated to the higher scales as priors within a Bayesian framework. This way, the measurements required from the high levels are reduced while making it possible to also update the estimated factors during the operation of the structure. The performance of the methodology is evaluated at different scales and compared with the direct use of compensation factors obtained from coupon studies only. It is demonstrated that the proposed methodology improves the fidelity of the compensation algorithm leading to a reduction in the uncertainty of the temperature-compensated signals. Based on the findings of the present study, the reduction in the uncertainty of the compensation improves the performance of both damage detection as well as damage localization in a large composite panel.

A CMOS SoC for Wireless Ultrasonic Power/Data Transfer and SHM Measurements on Structures

This paper describes a highly-integrated CMOS system-on-chip (SoC) for active structural health monitoring (SHM). The chip integrates ultrasonic power and bidirectional half-duplex data transfer, a power management unit (PMU), and an ultrasound transceiver to enable wireless ultrasonically-coupled sensor SHM networks on structures. The PMU includes an active bias-flip rectifier with off-delay compensation, a high-efficiency dual-path DC-DC converter with inductor time-sharing, and five switched-capacitor DC-DC converters to generate multi-level spectrally band-limited pulses for guided-wave SHM. The chip was fabricated in a standard 180 nm process and has a die area of 2 × 2 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Test results show power conversion efficiency (PCE) > 85% for the active rectifier, > 70% for the inductive DC-DC converter, and > 60% for the switched-capacitor DC-DC converters. Output pulses have a peak-to-sidelobe ratio (PSL) > 30 dB and worst-case out-of-band emissions < -30 dB, respectively. The SoC was integrated with a low-power microcontroller and passive components to realize miniaturized (15 mm × 30 mm) wireless SHM nodes. A set of nodes was deployed on an SHM test-bed (carbon fiber reinforced polymer sheet) representing an airframe panel. Tests on this wireless network confirm both long-range ultrasound power/data transfer and the ability to detect structural damage.

Application of compressed sensing in the guided wave structural health monitoring of switch rails

Switch rails are weak but essential components of high-speed rail (HSR) systems. In the condition-based maintenance of HSR, ultrasonic guided wave (UGW) on-line monitoring technology is widely used in judging real-time operating conditions; however, it always generates large amounts of data. Too much data bring significant challenges, such as too many unnecessary costs of energy, storage, and network bandwidth in the structural health monitoring of switch rails, making it challenging to realize embedded sensor networks with high durability and low power consumption. Furthermore, the structural damage occurs relatively less over the long-term, indicating that these measurements are inherently sparse. The sparseness of structural damage makes them attractive for the compressed sensing technique. This study proposes a novel data compression and reconstruction method to meet the challenges and reduce the amount of data transmitted by sensor networks and maintain their accuracies simultaneously. First, a lightweight data dictionary is constructed to perform a sparse decomposition of UGW signals according to the characteristics of UGW propagation. Second, an effective sampling method based on a sparse random matrix is designed for sub-Nyquist sampling and compressing UGW signals. Third, a novel block adaptive matching pursuing algorithm is proposed to reconstruct UGW signals from compressed data. Finally, numerical signals, finite element simulation, and several actual monitoring experiments on the foot of a switch rail are conducted to verify the effectiveness and accuracy of the proposed method. The influence of different compression ratios and block sizes on the reconstruction performance of guided wave signals is investigated. The results indicate that the proposed method can sample UGW signals with much lower requirements than the Nyquist sampling theorem and is superior to other novel algorithms.

Long-term guided wave structural health monitoring in an uncontrolled environment through long short-term principal component analysis

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.