Impact localization in composite structures with guided wave and 1D convolutional neural network

Abstract

Low velocity impacts may cause damages to the composite material. Piezoelectric transducers can be mounted onto the plate surface to monitor the impact by recording the impact induced guided waves. In this study, an impact localization algorithm based on 1D convolutional neural network is proposed. To test the effectiveness of the proposed algorithm, 4 PZTs were glued to a composite plate to monitor a region of 200 mm × 200 mm, and an impact experiment was conducted by dropping a glass ball at 117 different locations on the plate. The time-domain guided wave signals obtained by 4 PZTs were used as input to the 1D convolutional neural network. The output of the network was the coordinates of impact. The trained network was afterwards applied to locate impacts at unseen locations, and the mean localization error is 15.2 mm.