Attention-based interpretable prototypical network towards small-sample damage identification using ultrasonic guided waves

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.