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.
Published Version
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