›› 2019, Vol. 58 ›› Issue (5): 8-16.doi: 10.13471/j.cnki.acta.snus.2019.05.002

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Fault diagnosis method of rolling bearings based on VMD and mahalanobis distance SVM

QIAO Meiying1,2,LIU Yuxiang2,LAN Jianyi2,3     

  1. 1.School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China;
    2. School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China;
    3.Collaborative Innovation Center of Coal Work Safety, Henan Province, Jiaozuo 454000, China
  • Received:2018-11-02 Online:2019-09-25 Published:2019-09-25

Abstract:

It is difficult to identify the early fault of the rolling bearings. A diagnosis method based on variational mode decomposition (VMD) and Mahalanobis distance support vector machine (SVM) is proposed. Firstly, the original vibration signal is de-noised by wavelet threshold method to obtain effective vibration signal. Secondly, according to the center frequency of each mode after VMD decomposition, the final number of decomposed layers is determined. At the same time, the energy characteristics are extracted from the decomposed variational modal components. Finally, in order to measure distance between samples more accurately, Mahalanobis distance is introduced into the calculation of the Gaussian kernel function of the SVM, and a Gaussian function kernel based on Mahalanobis distance is established, which is used to support the vector machine classifier. Improved SVM is employed to identify the running state of the bearing, the experimental results show that the proposed method has high accuracy in identifying the normal state, the inner ring, the outer ring and the ball body fault of bearings.

Key words: bearing fault diagnosis, wavelet threshold method, variational mode decomposition, support vector machine, Mahalanobis distance

CLC Number: