Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (2): 52-58.doi: 10.16180/j.cnki.issn1007-7820.2022.02.009

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Sleeper Diseases Diagnosis Based on Permutation Entropy and Support Vector Machine

SHAO Zhihui,YANG Jian,YUAN Tianchen,WU Weijia   

  1. School of Urban Railway Transportation,Shanghai University of Engineering Science,Shanghai 201620,China
  • Received:2020-10-28 Online:2022-02-15 Published:2022-02-24
  • Supported by:
    National Natural Science Foundation of China(11802170);Shanghai Chenguang Project(18CG66);Natural Science Foundation of Shanghai(19ZR1421700)


This study proposes a sleeper diseases diagnosis method based on permutation entropy and support vector machine. This method obtains the vibration acceleration of the sleeper by establishing a vehicle-track coupled vibration model, uses the permutation entropy algorithm to extract the vibration response characteristic indexes under different diseases of the sleeper, and takes the normalized permutation entropy characteristic index set as input. Based on the support vector machine optimized by genetic algorithm, this method diagnoses and classifies the service status of sleepers, and realizes the diagnosis of different diseases of sleepers. The simulation results show that the accuracy of the method for the identification of sleeper diseases can reach more than 90%, and the recognition accuracy can reach 97.5% for the part of track irregularity spectrum excitation and the service state of the train speed. Therefore, the proposed method can effectively diagnose sleeper diseases and provide a certain method basis for online monitoring and intelligent early warning of track structure service status.

Key words: vibration response, sleeper faults, feature extraction, permutation entropy, normalized, genetic algorithm, support vector machine, disease diagnosis

CLC Number: 

  • TP391.4