Using acoustic emission technique for structural health monitoring of laminate composite: A novel CNN-LSTM framework

Abstract

This research has developed a real-time end-to-end deep learning model for structural health monitoring (SHM) method for composite impact damage diagnosis based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The acoustic emission (AE) signals collected under low-velocity impacts by means of piezoelectric sensors on composite materials are used for training deep learning networks. Based on the impact load curves, specimens are categorized into minor failure, intermediate failure, and severe failure. The convolved signals are segmented and reconstructed at a given length for the following LSTM module. The average accuracies for basic CNN, CNN– Recurrent Neural Network (RNN), CNN-LSTM, and CNN– Gated Recurrent Unit (GRU) are respectively 88.7 %, 92.6 %, 98 %, and 95.4 %. A sensitivity analysis on sub-signal length was conducted on the CNN-LSTM model, revealing that the model achieved its best performance when the sub-signal length was set at 16. The model attained prediction accuracies of 97.4 %, 100 %, and 100 %, respectively, for minor failure, intermediate failure, and severe failure cases.