Damage localization method using ultrasonic lamb waves and Wav2Vec2.0 neural network

Frontiers in Materials

Materials

Zhiqiang Wang¹  Yaohua Mei²  Yuhui Xing²  Hui Zhang²  Guopeng Fan²  Lubin Qian²  Sihao Liu³  Xinlong Liu⁴ 

[1] Luoyang Sunrui Rubber and Plastic Technology Co., Ltd., Luoyang, Henan, China;School of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai, China;Shanghai Urban Railway Operation Co., Ltd., Shanghai, China;Wuhan Ruimin Testing Technology Co., Ltd., Wuhan, Henan, China;

关键词: lamb wave;    Wav2Vec2.0 neural network;    ultrasonic testing;    structural health monitoring;    defect identification;   

DOI  :  10.3389/fmats.2023.1212909

 received in 2023-04-27, accepted in 2023-07-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

In this paper, a Wav2Vec2.0 neural network based on an attention mechanism is proposed to locate defects in array ultrasonic testing signals. This method does not require knowledge of the a priori condition of the sample sound velocity or the feature extraction of ultrasonic scattering signals. First, an array piezoelectric ultrasonic testing system is used to detect a signal through hole defects at different positions in the plate structure. Then, three different neural networks—1D-CNN, Muti-Transformer, and Wav2Vec2.0—are used to locate the defects in the collected ultrasonic testing data. The performance of the network is verified with the data set collected through finite element simulation and the experimental system, and the identification accuracy and the calculation efficiency of different networks are compared and analyzed. To provide a solution for the poor balance of the experimental data set and the weak noise resistance of the simulation data set, a data set expansion method based on time domain transformation technology is proposed. The research results show that, the positioning accuracy of the Wav2Vec2.0 neural network proposed in this article is 98.46%, and the positioning accuracy is superior to Muti Transformer and ID-CNN.

【 授权许可】

Unknown   
Copyright © 2023 Qian, Liu, Fan, Liu, Zhang, Mei, Xing and Wang.