Research on fault diagnosis of induction motor based KPCA and RVM

被引：0

作者：

Yang T.-G. ^{[1
,2
]}

Gui W.-H. ^{[2
]}

机构：

[1] College of Mechanical and Electrical Engineering, Hunan City University, Yiyang

[2] College of Information Science and Engineering, Central South University, Changsha

来源：

Dianji yu Kongzhi Xuebao | / 9卷 / 89-95期

关键词：

Faults diagnosis; Induction motor; Kernel principal component analysis; Releveant vector machine;

D O I：

10.15938/j.emc.2016.09.013

中图分类号：

学科分类号：

摘要：

According to the characteristics of induction motor, such as nonlinear, strong coupling and time-varying, a fault diagnosis method based on kernel principal component analysis (KPCA) and relevance vector machine (RVM) was proposed. Firstly, the induction motor stator current was decomposed using wavelet, and the KPCA approach was adopted to extract the feature vector and remove the redundant information effectively. Secondly, the relevance vector machine was used to classify the fault feature vectors and to identify the states of induction motor. The experiments were setup to verify the feasibility and practicability of this method under different running condition. The results show that the method based on KPCA-RVM has better classification effectively and better ability of generalization than other three methods and is an effective method for induction motor fault diagnosis. © 2016, Harbin University of Science and Technology Publication. All right reserved.

引用

页码：89 / 95

页数：6

共 18 条

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