Towards ultrasonic guided wave fine-grained damage detection on hierarchical multi-label classification network

Abstract

This study advocates the hierarchical multi-label classification (HMC) network for fine-grained damage detection using ultrasonic guided wave. Existing deep learning methods only focus on one aspect of the damage type or the degree of damage progression, and both are important for the long-term safe operation of the structure. To address this limitation, an ultrasonic guided wave HMC network (GHmcNet) and its lightweight version (L-GHmcNet) are developed. The essential designs of the proposed methods are derived from two motivations: (1) achieving robust fine-grained damage classification; (2) designing a lightweight network. The hierarchical labels (defect type-depth-size) are constructed on guided wave data. Inspired by the work of image classification, residual connections are introduced to transfer and fuse features between parent and children categories. The GHmcNet has three output channels and a weighted cross-entropy loss function is imposed on each channel. The weights of the corresponding loss are automatically adjusted during training. To reduce the parameters, we improve the structure of GHmcNet by applying different numbers of convolutional layers and channels at different levels based on the number of classes in each level, proposing the lightweight model L-GHmcNet. Both numerical and experimental studies are carried out, in which the dataset contains signals of different damage types, depths and sizes. The results show the superiority of the proposed methods in terms of accuracy at three levels compared to state-of-the-art methods. Moreover, the high accuracy classification results of L-GHmcNet demonstrate that the proposed lightweight network is a successful attempt.