Based on wavelet packet and BP neural network of aircraft cable insulation layer defect type identification

Biao Yao; Chuanxian Chen; Yang An; Zhigang Qu

doi:10.1117/12.2645597

23 August 2022 Based on wavelet packet and BP neural network of aircraft cable insulation layer defect type identification

Biao Yao, Chuanxian Chen, Yang An, Zhigang Qu

Author Affiliations +

Proceedings Volume 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022); 123051H (2022) https://doi.org/10.1117/12.2645597
Event: International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 2022, Hangzhou, China

Abstract

Aircraft cables are widely used in the field of aeronautics and astronautics to provide power or contact information between various systems. Different types of defects inevitably occur in the cable insulation layer during actual use and correspondingly relevant measures should be taken. Therefore, it is essential to judge the defect type in the cable insulation layer. In this paper, an aircraft cable insulation layer defect detection system based on ultrasonic guided waves (UGWs) was built to collect defect reflected signals. The wavelet packet decomposition was used to extract the normalized energy of a reflected signal as its eigenvectors which could effectively distinguish defect types. The identification accuracy of aircraft cable defect types could reach 92.86% by using BP neural network.

Citation Download Citation

Biao Yao, Chuanxian Chen, Yang An, and Zhigang Qu "Based on wavelet packet and BP neural network of aircraft cable insulation layer defect type identification", Proc. SPIE 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 123051H (23 August 2022); https://doi.org/10.1117/12.2645597

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