Residual life prediction of multi-layer composite coatings based on vector quantised and attention-variational AutoEncoder network

Abstract

ABSTRACT The quality of multi-layer composite coatings (MLCC) directly affects the reliability of equipment. Ultrasonic C-scan has advantages of non-destructive and high-precision. The accurate determination of residual life (RL) from C-scan images of coatings is hindered by the difficulty of feature extraction. To address this issue, a RL prediction method for MLCC based on the Vector Quantised and Attention-Variational AutoEncoder (VQA-VAE) network is proposed. First, by incorporating attention mechanism and residual module, the VQA-VAE network can perform unsupervised feature extraction on C-scan images effectively. Then, the features are fused by two-dimensional entropy weighting method to get the health index (HI), which can be used to establish the RL prediction model. To verify the effectiveness of this method, C-scan is performed on MLCC by ultrasonic microscope to obtain C-scan images, and RL of MLCC is predicted. The experiment results demonstrate that this method achieves higher accuracy in predicting RL of MLCC. In a word, this is a non-destructive, high-efficiency, high-precision, and large-scale measurement technique for MLCC.