Intelligent recognition of acoustic emission signals from damage of glass fiber-reinforced plastics

Abstract

Glass fiber-reinforced plastics (GFRP) is widely used in many industrial fields. When acoustic emission (AE) technology is applied for dynamic monitoring, the interfering signals often affect the damage evaluation results, which significantly influences industrial production safety. In this work, an effective intelligent recognition method for AE signals from the GFRP damage is proposed. Firstly, the wavelet packet analysis method is used to study the characteristic difference in frequency domain between the interfering and AE signals, which can be characterized by feature vector. Then, the model of back-propagation neural network (BPNN) is constructed. The number of nodes in the input layer is determined according to the feature vector, and the feature vectors from different types of signals are input into the BPNN for training. Finally, the wavelet packet feature vectors of the signals collected from the experiment are input into the trained BPNN for intelligent recognition. The accuracy rate of the proposed method reaches to 97.5%, which implies that the proposed method can be used for dynamic and accurate monitoring of GFRP structures.

Effects of thermal aging on damage evolution behavior of glass fiber reinforced composites with multilayer graphene by acoustic emission

“…21,22 At present, some scholars have used the ML method to analyze AE signals and establish prediction models. Li et al 23 established a back propagation neural network (BPNN) model to intelligently identify the damage modes of GFRP composites. Xu et al 24 evaluated the level of matrix degradation of GFRP composites in the thermal environment by using the AE technique.…”