Accurate diagnosis of rolling bearing based on wavelet packet and genetic-support vector machine

Author

Xu, Yunjie ; Xiu, Shudong

Author_Institution

Coll. of Eng., Zhejiang Forestry Univ., LinAn, China

fYear

2010

fDate

26-28 June 2010

Firstpage

5589

Lastpage

5591

Abstract

This paper studies on the combination usage of wavelet packet and artificial genetic-support vector machine in the fault diagnosis of ball bearing. Energy eigenvector of frequency domain is extracted using wavelet packet analysis method. Fault state of ball bearing is identified by using radial basis function genetic-support vector machine. The test results show that this GSVM model is effective to detect fault of ball bearing.

Keywords

ball bearings; fault diagnosis; mechanical engineering computing; radial basis function networks; rolling bearings; support vector machines; GSVM model; accurate diagnosis; ball bearing; energy eigenvector; fault diagnosis; frequency domain; genetic-support vector machine; radial basis function; rolling bearing; wavelet packet analysis method; Application software; Ball bearings; Diagnostic expert systems; Educational institutions; Fault diagnosis; Rolling bearings; Support vector machine classification; Support vector machines; Testing; Wavelet packets; bearing; fault diagnosis; genetic-support vector machine; wavelet packet;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechanic Automation and Control Engineering (MACE), 2010 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-7737-1

Type

conf

DOI

10.1109/MACE.2010.5535701

Filename

5535701