JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (1): 7-14.doi: 10.6040/j.issn.1672-3961.1.2016.330

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Condition recognition of high-speed train based on multi-view classification ensemble

GUO Chao1, YANG Yan1*, JIANG Yongquan2, SONG Yi1   

  1. 1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, Sichuan, China;
    2. State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, Sichuan, China
  • Received:2016-03-31 Online:2017-02-20 Published:2016-03-31

Abstract: To solve the problem about the incompletion of feature extraction and inaccuracy of the identification performance of traditional method, a multi-view classification ensemble method(MVCE)for condition recognition of high speed train was proposed. The method combined with multi-view feature extraction and classification ensemble technology. For condition recognition, the wavelet energy, spectral coefficients and ensemble empirical mode decomposition fuzzy entropy were extracted from three angles: the characteristics of the signal, the frequency domain and the time-frequency domain. The Fisher ratio was used to perform feature selection for the frequency domain features of the high speed train vibration signal, then the feature of the three views were constructed collectively. The least square support vector machine(LSSVM)and the K nearest neighbor(KNN)classifiers were used to recognize each view. The output results of multiple classifiers were integrated by using the classification entropy voting principle. The experimental results showed that the average recognition rate of the proposed method on the simulation data and the laboratory data were 89.18% and 90.87% respectively. Meanwhile, the comparative results illustrated the completeness of the features extracted by the method and the validity of the ensemble model with diversity.

Key words: multi-view, classification ensemble, high-speed train, condition recognition, feature extraction

CLC Number: 

  • TP391
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