Lamb Wave-based Damage Detection Research Articles

Baseline-free assisted lamb wave-based damage detection in CFRP composites using graph convolutional networks and Transformer models

Carbon fiber-reinforced polymer (CFRP) composites are widely used in aerospace but are susceptible to damage, threatening structural safety. Thus, developing an effective structural health monitoring system is essential. This study presents CFRP-former, a novel deep learning framework that detects damage in CFRP composites by constructing spatio-temporal representation graphs of Lamb waves (LWs). The CFRP-former comprises three key modules: graph convolutional neural network (GCNN), a multi-layer perceptron (MLP), and a Transformer Encoder. The GCNN models the topological relationships between LW nodes and sensors. The MLP reduces data dimensionality while preserving key information for subsequent analysis. Meanwhile, the Transformer Encoder, employing multi-head attention mechanisms, captures global time-series patterns in the LW signals. Additionally, the CFRP-former framework includes a parallel module that separately predicts both the size and location of damage. Experiments using only four sensors show that CFRP-former achieves high accuracy in detecting and quantifying damage, with robustness confirmed through ablation tests.

Modeling and designing of a phased array comprising piezoelectric stack transducer for ultrasonic Lamb wave-based damage detection

Modeling and designing of a phased array comprising piezoelectric stack transducer for ultrasonic Lamb wave-based damage detection

Lamb wave-based damage assessment for composite laminates using a deep learning approach

With the increasing utilization of composite materials due to their superior properties, the need for efficient structural health monitoring techniques rises rapidly to ensure the integrity and reliability of composite structures. Deep learning approaches have great potential applications for Lamb wave-based damage detection. However, it remains challenging to quantitatively detect and characterize damage such as delamination in multi-layered structures. These deep learning architectures still lack a certain degree of physical interpretability. In this study, a convolutional sparse coding-based UNet (CSCUNet) is proposed for ultrasonic Lamb wave-based damage assessment in composite laminates. A low-resolution image is generated using delay-and-sum algorithm based on Lamb waves acquired by transducer array. The encoder-decoder framework in the proposed CSCUNet enables the transformation of low-resolution input image to high-resolution damage image. In addition, the multi-layer convolutional sparse coding block is introduced into encoder of the CSCUNet to improve both performance and interpretability of the model. The proposed method is tested on both numerical and experimental data acquired on the surface of composite specimen. The results demonstrate its effectiveness in identifying the delamination location, size, and shape. The network has powerful feature extraction capability and enhanced interpretability, enabling high-resolution imaging and contour evaluation of composite material damage.

Lamb Wave-Based Structural Damage Detection: A Time Series Approach Using Cointegration.

Although Lamb waves have found extensive use in structural damage detection, their practical applications remain limited. This limitation primarily arises from the intricate nature of Lamb wave propagation modes and the effect of temperature variations. Therefore, rather than directly inspecting and interpreting Lamb wave responses for insights into the structural health, this study proposes a novel approach, based on a two-step cointegration-based computation procedure, for structural damage evaluation using Lamb wave data represented as time series that exhibit some common trends. The first step involves the composition of Lamb wave series sharing a common upward (or downward) trend of temperature. In the second step, the cointegration analysis is applied for each group of Lamb wave series, which represents a certain condition of damage. So, a cointegration analysis model of Lamb wave series is created for each damage condition. The geometrical and statistical features of Lamb wave series and cointegration residual series are used for detecting and distinguishing damage conditions. These features include the shape, peak-to-peak amplitude, and variance of the series. The validity of this method is confirmed through its application to the Lamb wave data collected from both undamaged and damaged aluminium plates subjected to temperature fluctuations. The proposed approach can find its application not only in Lamb wave-based damage detection, but also in other structural health monitoring (SHM) systems where the data can be arranged in the form of sharing common environmental and/or operational trends.

Lamb wave based damage imaging under nonlinear chirp excitation

Considering a trade-off between temporal-spatial resolution and multi-mode nature of Lamb waves, tone bursts with short durations are usually used as excitations in Lamb wave based damage detection. A short-duration excitation usually requires a large amplitude to carry sufficient energy so as to obtain response signals with enough signal-to-noise ratio and cover a large inspection area. In this paper, an alternative Lamb wave damage imaging method using nonlinear chirp (nonlinear frequency modulation, NLFM) excitation with a long duration and a small amplitude is proposed. The signal processing techniques of pulse compression and dispersion compensation are adopted to compress the long-duration wave packets of response signals into short ones. Compared with conventional tone burst excitations with short durations and small amplitudes, due to the long duration of the nonlinear chirp excitation and the use of pulse compression, sufficient energy can be applied to transducers under small amplitude excitations so the image contrast in imaging will not degrade. Furthermore, as large amplitude excitations are no longer required, high voltage amplifiers are not necessary so the hardware of the Lamb wave testing system is simplified. Experiments on a carbon steel plate with an artificial crack are carried out and Lamb wave signals are collected using a linear array consisting of nine PZTs. Experimental results under the NLFM signal and conventional tone bursts are provided. Experimental results show that under the condition of the same excitation amplitude, the proposed method under the NLFM excitation can achieve better imaging quality compared with methods under conventional tone bursts.

A Lamb wave time-reversal field reconstruction method for damage detection with automatic focusing determination

This study proposes a wavefield reconstruction method with a time-reversal operation (WR-TR) based on the Lamb wave to detect the damage in the plate. Currently, it is challenging to implement the wavefield reconstruction method for damage detection because of two issues. One is how to quickly simulate the Lamb wavefield. The other is how to determine the focusing time for searching the desired frame from wavefield animation, which can show the location and size of damage. In response, this study introduces a multi-modal superposition finite difference time domain method (MS-FDTD) to simulate the Lamb waves propagation with little calculation cost, which facilitates damage imaging output quickly. In addition, a maximum energy frame method (MEF) is presented to automatically determine the focusing time from wavefield animation, enabling the detection of multiple damage points. The simulations and experiments have demonstrated good noise robustness, anti-distortion ability, and broad applicability with dense or sparse array layouts. Moreover, a detailed comparison between the proposed method and other four Lamb wave-based damage detection methods is evaluated in this paper.

An Efficient Lightweight Deep-Learning Approach for Guided Lamb Wave-Based Damage Detection in Composite Structures

Woven fabric composite structures are applied in a wide range of industrial applications. Composite structures are vulnerable to damage from working in complex conditions and environments, which threatens the safety of the in-service structure. Damage detection based on Lamb waves is one of the most promising structural health monitoring (SHM) techniques for composite materials. In this paper, based on guided Lamb waves, a lightweight deep-learning approach is proposed for identifying damaged regions in woven fabric composite structures. The designed deep neural networks are built using group convolution and depthwise separated convolution, which can reduce the parameters considerably. The input of this model is a multi-channel matrix transformed by a one-dimensional guided wave signal. In addition, channel shuffling is introduced to increase the interaction between features, and a multi-head self-attention module is designed to increase the model’s global modeling capabilities. The relevant experimental results show that the proposed SHM approach can achieve a recognition accuracy of 100% after only eight epochs of training, and the proposed LCANet has only 4.10% of the parameters of contrastive SHM methods, which further validates the effectiveness and reliability of the proposed method.

FEM Simulation-Based Adversarial Domain Adaptation for Fatigue Crack Detection Using Lamb Wave.

Lamb wave-based damage detection technology shows great potential for structural integrity assessment. However, conventional damage features based damage detection methods and data-driven intelligent damage detection methods highly rely on expert knowledge and sufficient labeled data for training, for which collecting is usually expensive and time-consuming. Therefore, this paper proposes an automated fatigue crack detection method using Lamb wave based on finite element method (FEM) and adversarial domain adaptation. FEM-simulation was used to obtain simulated response signals under various conditions to solve the problem of the insufficient labeled data in practice. Due to the distribution discrepancy between simulated signals and experimental signals, the detection performance of classifier just trained with simulated signals will drop sharply on the experimental signals. Then, Domain-adversarial neural network (DANN) with maximum mean discrepancy (MMD) was used to achieve discriminative and domain-invariant feature extraction between simulation source domain and experiment target domain, and the unlabeled experimental signals samples will be accurately classified. The proposed method is validated by fatigue tests on center-hole metal specimens. The results show that the proposed method presents superior detection ability compared to other methods and can be used as an effective tool for cross-domain damage detection.

A Baseline-Free Lamb Wave Damage Localization Method Based on Compressed Sensing

In conventional Lamb wave-based damage detection, estimation is generally performed using the difference between the measured and baseline signals. However, it is difficult to maintain consistent measurement conditions between the measured and baseline signals, likely leading to large measurement errors. This paper proposed a baseline-free Lamb wave-based detection method for locating damage to structures. The damage scattering waves are extracted according to their features in the frequency-wavenumber spectrum and the damage locations are visualized using probability imaging. To reduce the complications of full wavefield acquisition, wavefield data are acquired using piezoelectric transducer (PZT) arrays at sampling rates much lower than the Nyquist frequency, and the original wavefield is reconstructed by applying compressed sensing. Experimental results on an aluminum plate indicate that the proposed method can provide the damage probability at each plate position without requiring a reference signal, suggesting its applicability to damage diagnosis of structures. Moreover, the proposed method achieves a compression ratio of 86.7% compared with the use of Nyquist sampling.

Damage detection in plate structures based on baseline construction of ultrasonic Lamb wave using designed mobile transducer set

The ultrasonic Lamb wave-based damage detection method is widely used in plate structures. Due to the multimode characteristic, the collected Lamb wave signals are very complicated, making it difficult to obtain damage information. This paper proposes a damage imaging algorithm based on the baseline construction of the Lamb wave for plate structures. A mobile transducer set including two transducers is designed for Lamb wave excitation and signal collection. To extract damage scattering signals, the response of the signal in healthy areas (the structural response signal) is measured as a temporarily constructed baseline signal. The structural response signal is obtained based on the identified healthy signals collected in different areas of the same structure. Damage scattering signals are extracted as the differences between the current signals and the structural response signal. Damage indexes are calculated based on the damage scattering signals to locate the damage with the imaging algorithm. A damage detection experiment is conducted on an aluminum plate to verify this proposed method. The imaging result is in good agreement with the damage location in the experiment. This paper provides a potential alternative for damage detection of plate structures in practice.

Imaging of sub-surface defect in CFRP laminate using A0-mode Lamb wave: Analytical, numerical and experimental studies

Lamb wave propagation in the anisotropic material is characterized by the prominent directivity of wave energy transfer governed by the fiber direction. Due to this anisotropic behavior, it is difficult to define the location of defects by using the arriving time of reflected signals. In this article, A0-mode Lamb wave-based damage detection technique has been illustrated which can detect the overlapping region of incident and scattered wave in the vicinity of the finite defect region in CFRP composite plate-like structure. A 5-cycle Hanning windowed tone burst of 30 kHz has been allowed to propagate through a 2 mm thickness [0/90]4S CFRP plate with subsurface cylindrical defect. In the near field region of the defect, the incoming and reflected wave overlaps and the dynamic shear strains of the out-of-plane displacement evaluated consequently. A covariance matrix is developed consisting of the shear strains. The proposed technique can detect the overlapping regions by measuring the determinant of covariance matrix, thus the image of the defect can be reconstructed. In this article, the analytical model of the proposed wavelet-based technique for the subsurface cylindrical defect is discussed and their physical meanings are investigated through numerical and experimental studies in a cross-ply laminate.

Lamb Wave-Based Damage Detection Using Artificial Neural Network and Automated Feature Extraction

Lamb Wave-Based Damage Detection Using Artificial Neural Network and Automated Feature Extraction

A multi-level damage classification technique of aircraft plate structures using Lamb wave-based deep transfer learning network

Lamb wave-based damage detection is one of the most promising structural health monitoring (SHM) technologies for aircraft structures. In this paper, a Lamb wave-based deep transfer learning network is developed for multi-level damage classification of plate-type structures. A one-dimensional convolutional neural network (1D-CNN) is employed to deeply mine the damage characteristics of complex Lamb wave signals with multiple modes and multiple boundary reflections. The concept of multi-level damage classification is carried out to get different results for different engineers, and a multi-task cascaded 1D-CNN architecture is established for three levels of damage classifications, which is corresponding to different SHM levels, i.e. identifying the damage presence, location, and severity, respectively. In the multi-task cascaded architecture, a fine-tune transfer learning concept is adopted to share partial structures and weight values among different classification models, which could greatly improve the efficiency of the model calculation. In the multi-level damage classification model, the one-dimensional Lamb wave scattering signals with different damage locations and sizes are used as the input without any preprocessing steps, while the classifications of the damage presence, location and size are designated as output of different levels. An experiment has been conducted to verify the proposed multi-level damage classification model. The experimental results show that the training time of this model is reduced by 35% and the accuracy of the proposed model is greater than 99%, which verifies the effectiveness and reliability of the proposed technique.

Advances in theoretical, experimental and applied studies on second-harmonic generation of ultrasonic Lamb waves

<p indent=0mm>Nondestructive evaluation and testing of material degradation or micro-damages in an early stage are essential for the maintenance of the safety-critical structures. Therefore, it is important to detect defects in a very early stage or even better to detect changes in the microstructure before the defect initiation even occurs. Linear ultrasonic characteristics are not sufficiently sensitive to the kind of microscopic degradation of the material. However, on the other hand, even very small imperfections can produce very significant excess nonlinearity, which can be orders of magnitude higher than the intrinsic nonlinearity of the intact material. The excess nonlinearity is produced mainly by the strong local nonlinearity of microcracks. In order to assess the damage in components at an early stage, the use of nonlinear acoustics has been shown to be the leading and one of the most promising techniques. Second harmonic generation is a typical acoustic nonlinear effect of ultrasonic waves, which can be used to assess the material degradation and detect the micro-defects in their early stages. Furthermore, ultrasonic Lamb wave has been widely used in large-scale industrial structures for health monitoring and material nondestructive testing. Possibility to inspect inaccessible or hidden areas and great cost-effectiveness are the key advantages of ultrasonic Lamb wave-based damage detection approaches. Second harmonic Lamb wave, which combines the advantages of Lamb wave and the high sensitivity of second harmonic waves to micro damage, is drawing increasing attention in recent years. The measurement of second harmonic Lamb waves is investigated as a promising feature extraction technique to characterize material nonlinearity in plate-like structures. However, the second harmonic fields of Lamb waves are much more complex due to the dispersion and multi-mode nature of propagating Lamb waves. In general, the effect of second harmonic generation is often very small and can easily be overlooked experimentally. Consequently, proper mode tuning with physically based feature is essential to enhance the efficiency of second harmonic Lamb wave generation and reception. In experimental work, it is also difficult to observe and measure the second harmonic Lamb waves. Although significant development has been made in theoretical analysis and experimental investigation of second Lamb waves, the use of second harmonic Lamb wave for structural damage assessments is still limited by a series of scientific and technical difficulties. In this paper, the theoretical and experimental advances concerning second harmonic Lamb wave are systematically reviewed. First, theoretical analysis of second harmonic Lamb waves based on nonlinear reflection of acoustic waves is reviewed. Then, a general approach of perturbation method and normal modal expansion for theoretical study of the second harmonic Lamb waves propagation in a plate is introduced. The physical insight of nonlinear feature among different Lamb modes is clarified. As revealed in these pieces of theoretical work, the second harmonic modes of Lamb waves display an accumulative effect when conditions of phase matching and non-zero power transfer from the fundamental mode to the second harmonic mode are satisfied. The conditions for strong nonlinear effect of Lamb wave are discussed. Experimental observation and measurement of second harmonic Lamb waves are reviewed. Cumulative effect and efficiency of second harmonic generation of Lamb waves are discussed. The guideline for choosing the Lamb modes with higher sensitivity to material nonlinearity is provided. Based on the theoretical and experimental studies, the applications of second harmonic Lamb waves for material damage evaluation are summarized. In addition, some obscure understanding and disputed issues during the study in this research area are clarified. Finally, prospective priorities and research trends of second harmonic Lamb waves are discussed.

Lamb wave-based damage detection of composite structures using deep convolutional neural network and continuous wavelet transform

Carbon fiber reinforced plastics composites have been widely used in aerospace, automotive and other fields for its superior performance. To avoid possible risks, Lamb waves-based structural heath monitoring techniques have been applied to detect the internal damage of these composites. However, due to their intrinsic high degree of anisotropy which makes the failure mode complicated under fatigue loads, an accurate and timely method for damage detection of these composites is still challenging to realize. This paper proposes a novel method for detecting the internal delamination of the carbon fiber reinforced plastics by combining deep convolutional neural network and continuous wavelet transform. This data-driven method can effectively leverage the large amount of data without relying on complex feature extraction. After converting the time series signal into a two-dimensional time–frequency image by the continuous wavelet transform technique, the convolutional neural network model is applied to classify the images. Based on the prediction results of different paths by the model, a damage localization method is also proposed for ply delamination. An experimental study is implemented to verify the effectiveness of this method. The result shows this method is a useful technique for precise diagnosis and positioning of delamination damage in the composite structures.

Development of PZT fiber rosette to locate acoustic source

Because of their electro-mechanical coupling property, Lead-Zirconate-Titanate (PZT) materials have been widely used for ultrasonic wave sensing and actuation in structural health monitoring applications. In this paper, a PZT rosette concept is proposed to conduct Lamb wave-based damage detection in panel-like structures by exploring its best directional sensing capability. First, a directivity study was conducted to investigate sensing of flexural Lamb wave propagation using a PZT fiber having d33 effects. Then, commercial off-the-shelf PZT fibers were polarized in-house in order to construct the PZT rosette configuration, in which three PZT fibers are oriented at 0°, 45°, 90°, respectively. Since Lamb wave responses are directly related to measured PZT fiber voltage signals, a simple interrogation scheme was developed to calculate principal strain direction in order to locate an acoustic source. Comprehensive tests were conducted to evaluate the performance of the proposed PZT rosette using an aluminum plate. It is shown that the PZT rosette is able to sense Lamb wave responses and accurately locate an acoustic source. We expect to further evaluate the PZT rosette performance when damages are introduced.

Lamb wave based damage detection in metallic plates using multi-headed 1-dimensional convolutional neural network

Lamb wave based damage diagnosis holds potential for real-time structural health monitoring; however, analysing the Lamb wave response possess challenge due to its complex physics. Data-driven machine learning (ML) algorithms are often more effective in identifying the damage-related features from these complex responses. However, in analysing such complex responses the ML algorithms requires extensive data pre-processing and are often not suitable for real-time damage detection. This paper presents a deep learning multi-headed 1-dimensional convolutional neural network (1D-CNN) architecture capable to operate directly on raw discrete time-domain Lamb wave signals recorded from a thin metallic plate. The multi-headed configuration consisting of two parallel 1D-CNN layers is capable to learn higher order damage-related features and enhances robustness of overall classification performance. To train the adopted 1D-CNN algorithm a diverse database is also constructed consisting 216 numerically and 24 experimentally generated responses of a thin 1.6 mm Al-5052 plate structure. The diversification of training database is achieved by varying parameters like scanning length, scanning frequency and adding different levels of white noises to the captured responses. Later, the trained 1D-CNN architecture is tested against two separated unseen test-databases. The first test database consist of experimentally generated 12 samples with notch-like damage and 12 samples of pristine condition. The proposed 1D-CNN classifier generalizes well on the unseen samples and decisively predicts the outcome for 23 out of 24 samples of first test database. The second test database consists of 108 unseen FE simulated samples capturing additional damage scenarios. In the second test phase, the model has correctly predicted the condition of all the 108 samples.

Machine Learning-Enriched Lamb Wave Approaches for Automated Damage Detection.

Lamb wave approaches have been accepted as efficiently non-destructive evaluations in structural health monitoring for identifying damage in different states. Despite significant efforts in signal process of Lamb waves, physics-based prediction is still a big challenge due to complexity nature of the Lamb wave when it propagates, scatters and disperses. Machine learning in recent years has created transformative opportunities for accelerating knowledge discovery and accurately disseminating information where conventional Lamb wave approaches cannot work. Therefore, the learning framework was proposed with a workflow from dataset generation, to sensitive feature extraction, to prediction model for lamb-wave-based damage detection. A total of 17 damage states in terms of different damage type, sizes and orientations were designed to train the feature extraction and sensitive feature selection. A machine learning method, support vector machine (SVM), was employed for the learning model. A grid searching (GS) technique was adopted to optimize the parameters of the SVM model. The results show that the machine learning-enriched Lamb wave-based damage detection method is an efficient and accuracy wave to identify the damage severity and orientation. Results demonstrated that different features generated from different domains had certain levels of sensitivity to damage, while the feature selection method revealed that time-frequency features and wavelet coefficients exhibited the highest damage-sensitivity. These features were also much more robust to noise. With increase of noise, the accuracy of the classification dramatically dropped.

Determination of Lamb wave phase velocity dispersion using time–frequency analysis

Accurate knowledge of dispersion relations is of vital importance for Lamb wave-based damage detection. In this paper, an experimental technique is proposed to obtain the Lamb waves phase velocity dispersion without prior knowledge of material properties. It requires measurements at only two neighboring positions, and can evaluate phase velocity dispersion over a wide frequency range. In that technique, the short-time chirp-Fourier transform is first applied to the Lamb wave signals to achieve a concentrated time–frequency representation. On this basis, ridge tracking is completed and the group delay (GD) of the interested modes is estimated accurately. Subsequently, the Vold–Kalman filter is introduced to extract these wave modes, and the instantaneous phase is estimated from each individual wave mode via Hilbert transform. Lastly, the phase velocity is obtained as a function of the propagation distance, GD and phase shift. The proposed technique is investigated in two different mediums: simulated signals as Lamb wave propagating in an aluminum plate and experimental signals as that in a carbon fiber-reinforced polymer laminate. The results demonstrate its effectiveness and efficiency.

Multi-feature Fusion and Damage Identification of Large Generator Stator Insulation Based on Lamb Wave Detection and SVM Method.

Due to the merits of Lamb wave to Structural Health Monitoring (SHM) of composite, the Lamb wave-based damage detection and identification technology show a potential solution for the insulation condition evaluation of large generator stator. This was performed in order to overcome the problem that it is difficult to effectively identify the stator insulation damage the using single feature of Lamb wave. In this paper, a damage identification method of stator insulation based on Lamb wave multi-feature fusion is presented. Firstly, the different damage features were extracted from time domain, frequency domain, and fractal dimension of lamb wave signals, respectively. The features of Lamb wave signals were extracted by Hilbert transform (HT), power spectral density (PSD), fast Fourier transform (FFT), and wavelet fractal dimension (WFD). Then, a machine learning method based on support vector machine (SVM) was used to fuse and reconstruct the multi-features of Lamb wave and furtherly identify damage type of stator insulation. Finally, the effect of typical stator insulation damage identification is verified by simulation and experiment.