Computer Science ›› 2014, Vol. 41 ›› Issue (1): 91-94.

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Fault Diagnosis of High-speed Rail Based on Approximate Entropy and Empirical Mode Decomposition

ZHAO Jing-jing,YANG Yan,LI Tian-rui,ZENG Jing and WEI Lai   

  • Online:2018-11-14 Published:2018-11-14

Abstract: The faults of anti-yaw damper,lateral damper and air spring are three kinds of common faults of high-speed rail.According to the non-stationary and nonlinear characteristic of three kinds of common faults of high-speed rail,approximate entropy and empirical mode decomposition were introduced to extract the feature of high-speed rail faults,and BP neural network was used as the model for the fault diagnosis of high-speed rail.The experimental results show that the proposed method is effective.In addition,the comparison experiment indicates that the fault diagnosis based on the combination of approximate entropy and empirical mode decomposition obtains better result than the fault diagnosis based on only one feature.

Key words: Feature extraction,Approximate entropy,Empirical mode decomposition,Neural network

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