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Fatigue damage prognosis studies typically involve updating a degradation model based on the collected measurements. The updated model can accurately depict the past degradation process, while it is always assumed to be capable of describing the future damage progression, and thus can be used for remaining useful life (RUL) prediction. However, this assumption cannot always hold true, due to the uncertainties arising from the degradation process. In this context, this paper proposes a scheme of prognostic-aided model updating in a particle filter (PF)-based prognostic framework, which empowers the updated model to capture both the historical and future degradation process, and consequently, enhances the prognostic performance. First, two degradation models are formulated using the physics-based damage state and the data-driven health indicator (HI), respectively. The former is then re-formulated in state space by incorporating a measurement model. Next, the state space model is updated by the latest measurement through PF, providing the RUL and lifetime percentage (LP) distributions. Finally, the particles of damage state and parameters are modified by comparing the LP distributions from the updated model and the latter degradation model. The feasibility and robustness of the proposed method are validated by an experimental study, where an aluminum lug joint subject to fatigue crack growth is monitored by a guided wave measurement system. [ABSTRACT FROM AUTHOR]

High-speed trains inevitably experience the impact of complex temperature loads during operation, which can easily result in cracks. Structural health monitoring methods based on Lamb waves are affected by the impact of temperature on their propagation mechanism, leading to difficulties in constructing accurate damage diagnostic models. This paper proposes a crack damage identification method based on adaptive multi-scale sample entropy under variable temperature environment. A sliding window method is proposed to obtain multiple wave packets in a Lamb wave, which avoids the disadvantage of insufficient information of a single wave packet and improves the robustness and reliability of diagnosis. A variance-based multiscale transform method is proposed to process Lamb wave signals, which reduces the sensitivity of Lamb wave signals to temperature compared to the traditional mean-based multiscale transform method. Lamb waves in different sliding windows are transformed at multiple scales to extract damage Multi-scale sample entropy (MSE) at different scales. The Quantum genetic algorithm (QGA) is introduced to optimize sliding windows and MSE, achieving adaptive MSE extraction under variable temperature environments. Based on the advantage of adaptive multiscale entropy, a quantitative crack diagnosis model is established. To verify the effectiveness of the proposed method, crack detection experiments under variable temperatures were conducted. The results show that the proposed adaptive MSE has good resistance to temperature changes and can accurately identify crack length. [ABSTRACT FROM AUTHOR]

• A particle filter-based prognostic method combining multiple features is shown. • An online feature selection criterion is proposed based on bias estimation. • The method is tested by fatigue testing of an Aluminum lug joint with Lamb wave monitoring. • The robustness of the proposed method is tested on different specimens. Damage prognosis generally resorts to damage quantification functions and evolution models to quantify the current damage state and to predict the future states and the remaining useful life (RUL). The former typically consists of a function describing the relationship between the damage state and a statistical feature extracted from the measured signals, thus the prognostic performance will strongly depend on the selection of a proper feature. Given the best feature may vary for different specimens or even at each time instant for the same specimen during damage progression, such selection is a challenging task but has received little investigation so far. In this context, this paper proposes a particle filter-based damage prognosis framework, which involves an online feature fusion and selection scheme. A prognostic model is considered for each feature, with a multivariate process equation, formulated using both a damage degradation function and a bias parameter, and a measurement equation linking the damage state and that feature considering a data-driven model and the bias. One PF is used to estimate the damage state, its evolution parameters, and the bias for each model. Then, at each step, the feature with the smallest estimated bias is selected as the best feature providing the most likely state vectors and is used to select the most likely samples of the damage state and growth parameters for predicting the RUL and for calculating the prior at the next step. The proposed prognostic framework is demonstrated by an experimental study, where an aluminum lug structure subject to fatigue crack growth is monitored by a Lamb wave measurement system. [ABSTRACT FROM AUTHOR]

Lamb wave-based detection has become a promising technique for structural health monitoring in plate-like structures. However, the dispersion effect of Lamb waves makes the wave packets elongated, which degrades the resolution for damage identification. Hence, it is necessary to develop effective methods for the dispersion removal of Lamb waves. Recently, deep learning has drawn much attention for Lamb wave detection due to its powerful feature extraction capability. Most current deep learning models are developed directly for damage classification or location estimation tasks on the Lamb wave signals, rather than signal processing for further manipulation. Therefore, in this paper, a novel approach based on a convolutional auto-encoder is proposed for the dispersion compensation of Lamb waves. The convolutional auto-encoder model is utilized to construct the mapping relationship between the dispersive signal and the time of flight of the wave packet. The dispersion compensated signal is reconstructed by combining the estimated time of flight from the network with the waveform of the excitation signal. Numerical and experimental validations on aluminum and composite plates are implemented to verify the effectiveness of the proposed method. Compared with conventional methods for dispersion compensation, the results demonstrate that our proposed method not only separates the overlapped wave packets but also enables dispersion compensation of multimodal and multi-packet Lamb wave signals. In addition, this method is still applicable in the case of inaccurate dispersion data due to the generalizability of the data-driven model. [ABSTRACT FROM AUTHOR]

[Display omitted] • Material early-stage local damage can be located simply and efficiently. • The reflected zero-frequency component is a strong phase reversal reflection. • Most of the second harmonic transmits through the damage zone and little is reflected. • The concave-convex property is very sensitive to the relative magnitude of the TOE. Degradation, such as micro-voids, dislocation, persistent bands, and micro-cracks, can give rise to local material nonlinearity and cause early-stage material local damage. The material nonlinearity of a plate can be represented as a distribution function. Depending on whether the nonlinearity distribution function of a plate is smooth or not, early-stage material local damage can be divided into gradual and mutation damage. Gradual damage is usually caused by lattice distortion, and the nonlinearity distribution function is smooth. In contrast, mutation damage is usually induced by a discontinuous interface, and the nonlinearity distribution function is not smooth but stepped. Mutation damage is more harmful than gradual damage. To ensure safety, it is important to locate and evaluate mutation damage. The conventional nonlinear Lamb wave technique has limitations in resolving the location and spatial distribution of mutation damage owing to several reasons. To address these issues, a novel method for locating mutation damage based on the zero-frequency component is proposed in this paper. The theoretical solution of the zero-frequency component generated by the Lamb wave pulse is derived based on certain ideal assumptions. Other complex behaviors of the zero-frequency component can be understood by the contribution of this theory. The region of mutation damage is modeled by enhancing local three-order elastic coefficients, and mutation damage can be located simply and efficiently by using the proposed zero-frequency component-based method. The proposed damage detection based on the zero-frequency component of nonlinear Lamb waves has significant advantages compared to the second harmonic and mixing-wave detection techniques. [ABSTRACT FROM AUTHOR]

Predictive maintenance, as one of the core components of Industry 4.0, takes a proactive approach to maintain machines and systems in good order to keep downtime to a minimum and the airline maintenance industry is not an exception to this. To achieve this goal, practices in Structural Health Monitoring (SHM) complement the existing Non-Destructive-Testing (NDT) have been established in the last decades. Recently, the increasing computational capability such as utilization of a graphical processing unit (GPU) in combination with advanced machine learning techniques such as deep learning has been one of the main drivers in the advancement of predictive analytics in condition monitoring. In our previous work, we proposed a novel approach using deep learning for guided wave based structural health called DeepSHM. As a study case, we treated an ultrasonic signal from guided Lamb wave SHM with a convolutional neural network (CNN). In that work, we only considered a single central frequency excitation. This led to a single governing wavelength which is normally good for the detection of a single damage size. In classical signal processing, applying a broader excitation frequency poses an analysis and interpretation nightmare because it contains more complex information and thus is difficult to understand. This problem can be overcome with deep learning; however, it creates another problem: while deep learning typically results in a more accurate result prediction, it is specifically made for solving only certain types of tasks. While many papers have already introduced deep learning for diagnostics, many of these works are only proposing novel predictive techniques, however the mathematical formalization is lacking, and we are not informed about why we should treat acoustic signal with deep learning. So, the basis of 'explainable AI' for SHM and NDT is currently lacking. For this reason, in this paper, we would like to extend our previous work into a more generalized. Rather than focusing on a novel technique, we propose a plausible theoretical perspective inspired from neuroscience for signal representation of deep learning framework to model machine perception in structural health monitoring (SHM), especially because SHM typically involves multiple sensory input from different sensing locations. To do this, we created a set of artificial data from a finite element model (FEM) and represented DeepSHM in two different ways: 1). Perpetual representation of observation and 2). Hierarchical structure of entities that is decomposable in a smaller sub-entity. Consequently, we assume two plausible models for DeepSHM: 1). Either it behaves as a single deciding actor since the observation is regarded as perpetual, and 2). Or it acts as a multiple actor with independent outputs since multiple sensors can form different output probabilities. These artificial data were split into several different input representations, classified into several damage scenarios and then trained with commonly used deep learning training parameters. We compare the performance metrics of each perception model to describe the training behavior of both representations. [ABSTRACT FROM AUTHOR]

The authors have developed a high-temperature ultrasonic sensing system with a regenerated fiber Bragg grating (RFBG) sensor, that was fabricated by annealing a fiber-optic Bragg grating (FBG). Different from the common FBG, the RFBG ultrasonic sensor is resistant to temperature as high as 900 °C. In this study, taking advantage of the RFBG sensor, we attempted to establish a laser-ultrasonic visualization method for in-situ damage diagnosis in heat-resistant materials at elevated temperatures. In the first part of this paper, after reporting the fabrication of the RFBG in detail, we validated that the RFBG sensor has reasonable sensitivity to ultrasonic measurement over a broad frequency range at high temperatures. The RFBG was then incorporated into a laser-ultrasonic visualizing inspector to visualize the ultrasonic wave propagating in a SiC plate with an artificial defect at 800 °C. An improved data processing method was employed to verify the reliability of the visualization result. In the processing method, the visualization result was transformed to the spectrum in the wavenumber-frequency domain with the help of three-dimensional fast Fourier transform. Referring to theoretically calculated dispersion curves of the Lamb wave propagating in the SiC plate, we identified S 0 and A 0 modes from the spectral results. In addition, the agreement between the theoretical results and the experimental results also verified the feasibility of our proposed system to visualize the ultrasonic wavefield in high-temperature environment. Moreover, we developed a window function to extract the wave modes from the visualization result based on the dispersion curves. The artificial defect in the SiC plate could be then clearly localized from the visualization results for the separated individual modes. [ABSTRACT FROM AUTHOR]

In this paper, to increase the performance of the sparse reconstruction method in real complex engineering structures an adaptive dictionary learning framework is proposed which updates the dictionary matrix, to allow improved compatibility with the complex structure. This proposed framework was developed by combining analytical modeling with training data sets and learning methods. An experimental evaluation of the proposed dictionary learning framework was performed on an anisotropic composite plate with a stiffener. In this experimental evaluation, a moving magnet was used as the artificial damage to capture the training data set, and both artificial damage in several locations and real impact damage was used for detection and location of the target damage. The obtained results confirmed the concept of the proposed dictionary learning framework for the improved health monitoring of complex structures. [ABSTRACT FROM AUTHOR]

• Efficiency limits of anomalous refractions of flexural waves is determined. • An inverse design method is developed for metasurfaces with perfect efficiencies. • Nonlocal coupling is analyzed as the mechanism of perfect efficiencies. • The concept of bipartite elastic metasurface is proposed. • Bianisotropy is introduced to realize one-way transmission and spatial filtering. Elastic metasurfaces have shown immense potential in exotic wavefront manipulations based on the generalized Snell's law (GSL), stimulating applications in various engineering fields such as vibration and noise reduction, structural health monitoring and signal processing. One recognized bottleneck is that the GSL can lead to limited efficiencies of metasurfaces at steep deflection angles, which thereby arouses intense efforts in realizing perfect efficiencies, however, mainly in acoustics and electromagnetics. In this paper, we propose a paradigm to design high-efficiency elastic metasurfaces beyond the GSL efficiency limit, for the refraction manipulations of flexural waves in thin plates. We first find that the theoretical efficiency limits of anomalous refractions of flexural waves approach to zero at large deflection angles, but are higher than those of acoustic and electromagnetic waves due to the inherent evanescent mode contributing to a better impedance matching. A genetic algorithm-based inverse method is then proposed to design metasurfaces with almost perfect efficiencies (> 95 %), by engineering the nonlocal coupling between neighboring unit cells, which works well for the coarse assembly of only two unit cells in one period. Furthermore, by introducing bianisotropy, we demonstrate advanced high-efficiency functions such as the asymmetric transmission and spatial filtering. This work may offer insights towards the design of high-efficiency and structurally simplified wave-based devices. [ABSTRACT FROM AUTHOR]

• A novel dispersive decomposition method named iterative frequency-domain envelope-tracking filter (IFETF) for structural health monitoring. • Frequency envelope tracking with joint-estimation technique to accurately extract the overlapped dispersive components. • Non-parameterized group delay (GD) refinement procedure to ameliorate the time–frequency resolution. • Adaptive ridge detection technique to automatically estimate the number of components and their GDs with less prior knowledge. • Comprehensive comparison among five related time–frequency analysis methods. Dispersive signals are omnipresent in structural health monitoring and nondestructive testing. Due to the strong dispersion and multimodal characteristics, such signals often contain multiple components with frequency-varying group delays (GDs) overlapping in time–frequency (TF) domain, bringing great challenges to the existing signal processing methods. This paper presents a novel dispersive signal decomposition method named iterative frequency-domain envelope-tracking filter (IFETF) to accurately estimate the dispersion curves and separate overlapped components. Specifically, an envelope tracking issue is introduced in frequency domain based on dispersive signal model. Then, GDs are refined by recovered frequency envelope, and an iterative processing procedure is designed to ameliorate the TF resolution. To initialize the IFETF, an adaptive ridge detection technique is introduced to automatically estimate the number of components and their GDs with little prior knowledge. In addition, a high-quality TF representation can be constructed according to the results of IFETF, unambiguously revealing TF patterns of a multi-component dispersive signal. The proposed method was demonstrated by numerical analysis and engineering applications on broadband Lamb wave inspection and rail damage detection. Compared with traditional TF analysis techniques, the results show that the IFETF provides higher accuracy and efficiency in signal reconstruction and GD estimation. [ABSTRACT FROM AUTHOR]

• This paper presents a deep learning network that inspects ultrasonic signals for defect detection. • The network is an adaptation of WaveNet, hence is based on causal dilated convolutional neural networks and residual blocks. • The network is shown to outperform both a widely used conventional analysis method and some competing deep learning algorithms. • The validation was performed on two datasets, one generated via finite element simulations on a steel plate, the other by actual experiments on a composite plate. • Significant improvements over the conventional method are obtained especially when testing occurs at temperatures well outside the range in the training set of signals. This paper presents a deep learning network that performs automatic detection of defects by inspecting full ultrasonic guided wave signals excited in plate structures. The findings show that the algorithm, which is an adaptation of WaveNet, and hence is based on causal dilated convolutional neural networks, is effectively able to learn features and/or patterns related to the presence of waves scattered from damage, thus eliminating the need for any feature engineering to be performed by human operators. The network outperformed the widely used conventional approach that combines the optimal baseline selection and baseline signal stretch compensation methods when tested on two different datasets. The first dataset consisted of finite element simulated Lamb wave signals acquired in a pitch-catch configuration on a steel plate across a 50 °C range of temperature variations, and the second was a publicly available experimental dataset of Lamb wave signals also acquired in pitch-catch mode on a composite plate with a 40 °C range of variations. The improvements over the conventional approach are particularly encouraging when analyzing signals at temperatures well outside the temperature range available in the set of baseline signals, hence suggesting that this class of algorithms can complement or substitute existing methods, especially when testing occurs at unseen environmental and operational conditions, or when the effects of sensor drift make conventional methods less effective. [ABSTRACT FROM AUTHOR]

In this paper, several deep fully convolutional neural networks for image segmentation such as residual UNet, VGG16 encoder–decoder, FCN-DenseNet, PSPNet, and GCN are employed for delamination detection and localisation in composite materials. All models were trained and validated on our previously generated dataset that resembles full wavefield measurements acquired by scanning laser Doppler vibrometer. Additionally, a thorough comparison between all presented models is provided based on several evaluation metrics. Furthermore, the models were verified on experimentally acquired data with a Teflon insert representing delamination showing that the developed models can be used for delamination size estimation. The achieved accuracy in the current implemented models surpasses the accuracy of previous models with an improvement up to 22.47% for delamination identification. • Deep semantic segmentation techniques for delamination identification in CFRP. • Comparative study of five fully convolutional neural networks. • The training on synthetic data resulted in good generalisation capabilities. • Validation on experimental data. [ABSTRACT FROM AUTHOR]

• An analytical framework for modeling nonlinear Lamb wave propagation is constructed. • Numerical and experimental time reversing (TR) results of nonlinear Lamb waves are presented. • A new method by comparing physical-virtual TR results for fatigue crack detection is put forward. This article presents the investigation of a nonlinear Lamb wave time reversing technique for fatigue crack detection and quantification. A 2D analytical framework is initially presented, modeling Lamb wave generation, propagation, wave crack linear and nonlinear interaction, and reception. This study extends the Time Reversal (TR) techniques into the realm of nonlinear Lamb waves. Due to the structural transfer function variation between the forward and backward transmission process, the Virtual Time Reversal (VTR) algorithm reveals obvious deviation for predicting nonlinear Lamb waves, given that it replaces the backward TR procedure with the forward transfer function. However, this study demonstrates that the difference between the physical nonlinear TR method and the conventional VTR algorithm proves to be sensitive to detect and quantify fatigue cracks. Fatigue tests on a thin aluminum plate with a rivet hole are conducted to induce a fatigue crack. The experimental results further illuminate that the proposed physical-virtual nonlinear Lamb wave TR technique possesses remarkable sensitivity to the nucleation and growth of fatigue cracks. The paper finishes with discussion, concluding remarks, and suggestions for future work. [ABSTRACT FROM AUTHOR]