Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization

Zhang, Han; Lin, Jing; Hua, Jiadong; Tong, Tong

doi:10.1177/14759217211044806

Cited by 14 publications

(8 citation statements)

References 49 publications

(65 reference statements)

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“…In our previous work, an interpretable Lamb wave convolutional sparse coding (LW-CSC) method was adopted for structural damage identification and localization. 25 The damage features are extracted according to multi-layer ISTA. Additionally, as mentioned before, the case of 0 unfoldings corresponds to the standard CNN.…”

Section: Discussionmentioning

confidence: 99%

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Damage localization with Lamb waves using dense convolutional sparse coding network

Zhang

Hua

Lin

et al. 2022

Structural Health Monitoring

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Composite materials are progressively employed in many safety-critical structural applications due to their superior properties. Structural health monitoring techniques based on Lamb waves have been utilized to assess the damages of composite structures. Recently, deep learning algorithms are adopted for damage detection and localization. Identifying valid damage-related features through neural networks is a crucial step in the analysis process. However, most implemented deep learning architectures are still lacking physical interpretability to some extent. In this paper, a dense convolutional sparse coding network (DCSCNet) is presented for Lamb wave-based damage localization in composite structures, providing a possibility to interpret current networks. In DCSCNet, narrowband Hanning windowed toneburst signals are utilized as kernels of the first convolutional layer to learn more meaningful features. Dense connection is theoretically demonstrated in the scope of DCSCNet, which gathers multiple feature maps directly to develop the potential of the network through feature reuse. The multi-layer iterative soft thresholding algorithm with the dense connection is then employed for solving the multi-layer convolutional sparse coding model. Moreover, effective Squeeze-Excitation is introduced as the channel attention module to boost the representational capability of the network. The experimental results demonstrate the high-performance and interpretable characteristics of the proposed DCSCNet, verifying its feasibility and effectiveness in Lamb wave-based damage localization of composite structures.

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Section: Discussionmentioning

confidence: 99%

“…In practice, sparse coding-based approaches have been widely utilized for Lamb wave damage localization. [23][24][25] Since the structure is mostly damage-free in normal circumstances, the recorded signals can be expressed with several nonzero coefficients containing effective damage information.…”

Section: Introductionmentioning

confidence: 99%

Damage localization with Lamb waves using dense convolutional sparse coding network

Zhang

Hua

Lin

et al. 2022

Structural Health Monitoring

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“…To increase the robustness of the network, data augmentation was employed by adding randomly distributed Gaussian noise to the images. 20 A hundred images per damage scenario were created, resulting in a total of 320 and 160 images for the rectangular and circular arrays, respectively, for each panel. The final size of the dataset is therefore 480 images of 7 × 10,568 pixels.…”

Section: Dataset Generationmentioning

confidence: 99%

Enhancing Lamb wave-based damage diagnosis in composite materials using a pseudo-damage boosted convolutional neural network approach

Gonzalez-Jimenez,

Lomazzi,

Junges

et al. 2023

Structural Health Monitoring

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Damage diagnosis of thin-walled structures has been successfully performed through methods based on tomography and machine learning-driven methods. According to traditional approaches, diagnostic signals are excited and sensed on the structure through a permanently installed network of sensors and are processed to obtain information about the damage. Good performance characterizes methods that process Lamb waves, which are described by long propagation distances and high sensitivity to anomalies. Most of the methods require extracting damage-sensitive features from the diagnostic signals to drive the damage diagnosis task. However, this process can lead to loss of information, and the choice of the specific feature to extract may introduce biases that hamper damage diagnosis. Furthermore, traditional approaches do not perform well when composites are considered, due to the anisotropy, inhomogeneity, and complex damage mechanisms shown by this type of material. To boost the performance of methods for damage diagnosis of composite plates, this work proposes a convolutional neural network (CNN)-based algorithm that localizes damage by processing Lamb waves. Different from other methods, the proposed method does not require extracting features from the acquired signals and allows localizing damage through the regression approach. The method was tested against experimental observations of Lamb waves propagating in two composite panels and in a hybrid panel, and the performance of two different sensor arrays was investigated. The pseudo-damage approach was used to generate large enough datasets for training the CNNs, and the performance of the framework was evaluated by localizing pseudo-damage and real damage determined by low-velocity impacts. The CNN-driven method accurately localized damage in all the considered scenarios, and it also outperformed traditional damage indices-based approaches, such as the reconstruction algorithm for probabilistic inspection of defects.

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“…Wu et al [14] proposed a method combining deep convolutional neural network and continuous wavelet transform to achieve the detection of internal damage of CFRP, the damaged Lamb wave signals were obtained by arranging piezoelectric (PZT) sensor network, and the Lamb wave signals were processed by the proposed method to realize the fatigue damage diagnosis. Zhang et al [15] proposed an interpretable Lamb wave convolutional sparse coding (LW-CSC) method for structural damage identification and localization, and the experimental results demonstrated that the method could improve classification accuracy, and achieve damage localization accurately. Gao and Hua [16] proposed a deep learning algorithm combining convolutional neural networks (CNN) with stacked autoencoders (SAE) and processed the broadband Lamb wave signals for material damage localization and quantification, and the effectiveness of the method through experiments was verified.…”

Section: Introductionmentioning

confidence: 99%

SN-SAE: a new damage diagnosis method for CFRP using Lamb wave

Wang

et al. 2023

Meas. Sci. Technol.

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The damage diagnosis of carbon fiber reinforced polymer (CFRP) using Lamb wave has been widely developed, but it is still a challenging task to obtain reliable damage diagnosis results by analysis of Lamb wave, the emergence of deep learning models provides an effective solution for this work. However, the internal covariate shift and overfitting exist in traditional deep networks. The SN-SAE deep neural network model is proposed by introducing stochastic normalization (SN) into stacked autoencoder (SAE). The signals of 28 different damage locations in the CFRP plate provided by the open platform are processed by SN-SAE, and the damage diagnosis at different locations is achieved. The validity of SN-SAE was further verified by data obtained through building an experimental platform. The results demonstrated that the SN-SAE model can achieve high test accuracy with only 15% of the data samples as training with limited data sample, which provides a simple and effective solution for damage diagnosis of composite plates.

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Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization

Cited by 14 publications

References 49 publications

Damage localization with Lamb waves using dense convolutional sparse coding network

Damage localization with Lamb waves using dense convolutional sparse coding network

Enhancing Lamb wave-based damage diagnosis in composite materials using a pseudo-damage boosted convolutional neural network approach

SN-SAE: a new damage diagnosis method for CFRP using Lamb wave

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