中山大学学报自然科学版 ›› 2019, Vol. 58 ›› Issue (5): 8-16.doi: 10.13471/j.cnki.acta.snus.2019.05.002

• 论文 • 上一篇    下一篇

基于VMD和马氏距离SVM的滚动轴承故障诊断

乔美英1,3, 刘宇翔1,兰建义2,3   

  1. 1.河南理工大学电气工程与自动化学院,河南 焦作 454000;
    2.河南理工大学能源科学与工程学院,河南 焦作 454000;
    3.煤炭安全生产河南省协同创新中心,河南 焦作454000
  • 收稿日期:2018-11-02 出版日期:2019-09-25 发布日期:2019-09-25
  • 通讯作者: 刘宇翔(1993年生),男;研究方向:故障诊断;E-mail: 641573672@qq.com

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

摘要:

针对滚动轴承早期故障识别较困难的问题,提出一种基于变分模态分解(VMD)和马氏距离支持向量机(SVM)的诊断方法。首先,采用小波阀值法对原始振动信号进行去噪处理,获得有效的振动信号。其次,根据VMD分解后每个模态的中心频率大小不同,确定最终分解层数。同时,从分解后的变分模态分量中提取能量特征。最后,为了对样本间进行距离度量,将马氏距离引入SVM的高斯核函数计算中,建立了一个基于马氏距离的高斯函数核,用于支持向量机分类器。利用改进的SVM对轴承的运行状态进行识别,实验结果表明所提方法在识别轴承正常状态、内圈、外圈以及滚珠体故障时,具有较高的准确率。

关键词: 轴承故障诊断, 小波阀值法, 变分模态分解, 支持向量机, 马氏距离

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

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