Complex CFRP damage detection with dual multiscale 1DCNNs and self-selective fusion technique of guided waves

Abstract

This article presents an innovative damage detection method, named DMS1DCNNs, based on new dual multiscale one-dimensional convolutional neural networks (MS1DCNNs) and a novel self-selective fusion technique for damage localization and quantification in complex carbon fiber reinforced plastic (CFRP) structures. Firstly, a new multiscale piecewise aggregate approximation algorithm (MIPA) is proposed to compress the guided wave (GW) signals and extract damage features at various time scales. Considering that signals from low-information sensing paths may interfere with the diagnostic performance of the model, a new method for self-selective fusion of GW sensing paths is proposed. This method can automatically fuse paths with more damage information and remove paths with less damage information. Then, a new DMS1DCNNs model is presented, capable of obtaining multiscale damage features in GW signals from both data and network levels. The model takes GW data as input and employs regression methods for direct prediction of the damage location and size. Finally, the method is validated using the GW testbed, and the mean relative error of location on complex CFRP is 8.32 % and the mean absolute error of quantification is 1.20 mm, superior to other state-of-the-art methods.