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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]

Data-driven deep learning approaches have been recently developed for guided wave-based structural health monitoring. However, the difficulty in collecting and labeling valid samples often leads to a small dataset in practical damage identification, lowering the performance of the trained model. In addition, conventional deep learning models often lack a certain degree of physical interpretability. In this study, an attention-based interpretable prototypical network is proposed for small-sample damage identification using ultrasonic guided waves. The prototypical network is utilized as the framework to calculate the prototype of each category and the similarity between samples based on metric space. Afterward, the channel attention module is constructed for feature extraction, which enables the network to highlight valid information across channels and alleviate the overfitting problem. Moreover, local interpretable model-agnostic explanation (LIME) is introduced to explain the intrinsic mechanism for damage identification performed by the network in terms of critical feature contributions. To implement efficient damage identification based on small data, both numerical and experimental studies are carried out, in which the dataset contains pinhole, crack, and corrosion damage. The classification performance shows that the proposed network can serve as an effective model to overcome the shortage of limited data for damage identification, and the LIME analysis significantly enhances the interpretability of the network. [ABSTRACT FROM AUTHOR]