Damage localization with Lamb waves using dense convolutional sparse coding network

Abstract

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.