Track Defects Recognition Based on Axle-Box Vibration Acceleration and Deep- Learning Techniques  

作　　者：Xianxian Yin Shimin Yin Yiming Bu Xiukun Wei 

机构地区：[1]School of Transportation Engineering,Shandong Jianzhu University,Jinan,250101,China [2]China Construction Eighth Bureau(Shandong)Design Consulting Co.,Ltd.,Jinan,250100,China [3]State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing,100044,China

出　　处：《Structural Durability & Health Monitoring》2024年第5期623-640,共18页结构耐久性与健康监测（英文）

基　　金：supported by the Doctoral Fund Project(Grant No.X22003Z).

摘　　要：As an important component of load transfer,various fatigue damages occur in the track as the rail service life and train traffic increase gradually,such as rail corrugation,rail joint damage,uneven thermite welds,rail squats fas-tener defects,etc.Real-time recognition of track defects plays a vital role in ensuring the safe and stable operation of rail transit.In this paper,an intelligent and innovative method is proposed to detect the track defects by using axle-box vibration acceleration and deep learning network,and the coexistence of the above-mentioned typical track defects in the track system is considered.Firstly,the dynamic relationship between the track defects(using the example of the fastening defects)and the axle-box vibration acceleration(ABVA)is investigated using the dynamic vehicle-track model.Then,a simulation model for the coupled dynamics of the vehicle and track with different track defects is established,and the wavelet power spectrum(WPS)analysis is performed for the vibra-tion acceleration signals of the axle box to extract the characteristic response.Lastly,using wavelet spectrum photos as input,an automatic detection technique based on the deep convolution neural network(DCNN)is sug-gested to realize the real-time intelligent detection and identification of various track problems.Thefindings demonstrate that the suggested approach achieves a 96.72%classification accuracy.

关 键 词：Track defects intelligent detection deep convolution neural network acceleration of axle-box vibration 

分 类 号：U213.2[交通运输工程—道路与铁道工程]