Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (2): 27-33.doi: 10.16180/j.cnki.issn1007-7820.2022.02.005

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Research on Track Structure Damage Identification Based on Support Vector Machine

WU Weijia,YANG Jian,YUAN Tianchen,SHAO Zhihui   

  1. School of Urban Railway Transportation,Shanghai University of Engineering Science,Shanghai 201620,China
  • Received:2020-10-01 Online:2022-02-15 Published:2022-02-24
  • Supported by:
    National Natural Science Foundation of China(11802170);Shanghai Morning Light Project(18CG66);Shanghai Natural Science Foundation(19ZR1421700)


The track structure is a key component that carries the load of the train. Once a disease occurs, it will directly affect the safety of the train. To solve this problem, a method for identifying the track structure disease based on a support vector machine is proposed. This method uses time-domain statistics and discrete wavelet transform to perform joint feature extraction on the vibration acceleration data of the sleeper under different working conditions of the track structure, such as normal state, unsupported sleeper and cement hardening, which reduces the dimensionality of data and provides the possibility for disease identification. The method also uses the support vector machine algorithm to identify the feature vector, and uses the grid search method to select the parameters of the support vector machine, so that the recognition accuracy rate reaches about 85%. The experimental results show that the proposed method can better identify different degrees of unsupported sleeper and cement hardening, and provide a technical basis for online early warning of track structure failure.

Key words: unsupported sleeper, cement hardening, disease identification, time-domain statistics, feature extraction, grid search, support vector machine

CLC Number: 

  • TP391.4