In order to ensure the safe operation of high-speed trains,there is an urgent need for a non-destructive testing tech-nology that can automatically and intelligently detect rail damage in trains.Therefore,this article uses a combination of deep learning methods and ultrasonic guided wave technology to quantitatively evaluate the depth of fatigue damage to rail heads.Firstly,a rail head fatigue damage detection system based on ultrasonic guided waves is constructed to obtain transmission wave signals of rail head fatigue damage at different depths and the interaction between ultrasonic guided waves.Then,wavelet time-frequency maps are obtained through wavelet transform,and three types of Gaussian white noise are added(Gaussian white noise is 0.1,0.3,and 0.5).The wavelet time-frequency maps are divided into 8∶2 training and testing sets.Finally,four deep learning models,GoogLeNet,Mobile-netv1,Mobilenetv2,and Mobilenetv3,are used to classify the depth of rail fatigue damage.The accuracy,recall,accuracy,and F1 score of the four models are compared.The experimental results show that the combination of Mobilenetv3 depth model and ultrasonic guided wave technology for quantitative detection of fatigue damage depth in rail heads has a recognition accuracy of 98％.This re-search provides a basis for the feasibility and reliability of combining deep learning models with ultrasonic guided wave technology for identifying deep detection of fatigue damage in steel rails.
关键词
钢轨轨头疲劳损伤/超声导波/小波变换/深度学习模型/定量检测
Key words
rail head fatigue damage/ultrasonic guided waves/wavelet transform/deep learning models/quantitative detection
维普
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