Computer Science ›› 2016, Vol. 43 ›› Issue (12): 281-286.doi: 10.11896/j.issn.1002-137X.2016.12.052

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Fault Analysis of High Speed Train Based on EDBN-SVM

GUO Chao, YANG Yan and JIN Wei-dong   

  • Online:2018-12-01 Published:2018-12-01

Abstract: As a new hot spot in the field of machine learning,deep learning has opened up new ideas for the research of fault diagnosis.In view of significance of fault analysis for high speed train,combining deep learning and ensemble lear-ning,a new fault diagnosis model based on EDBN-SVM(Ensemble Deep Belief Network-Support Vector Machine)was proposed.Firstly,we preprocessed the vibration signal of high speed train by fast fourier transform (FFT).Secondly,we analyzed the parameters of the EDBN-SVM model,then we set the FFT coefficients as the input of the visible layer of EDBN-SVM model,and used the model to learn high-level features layer by layer.Finally,we utilized multiple SVM classifiers to recognize faults,and combined the recognition results.In order to evaluate the validity of this method,we selected the laboratory data and the simulation data to conduct experiments,and compared it with the traditional fault analysis methods.The results show that the fault recognition effect and the stability of this method are better than traditional methods.

Key words: High speed train,Fault analysis,Fast Fourier transform,Deep belief network

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