Rolling element bearings fault classification based on SVM and feature evaluation

Author

Sui, Wen-tao ; Zhang, Dan

Author_Institution

Sch. of Mech. Eng., Shandong Univ. of Technol., Zibo, China

Volume

1

fYear

2009

fDate

12-15 July 2009

Firstpage

450

Lastpage

453

Abstract

A new method of fault diagnosis based on support vector machine (SVM) and feature evaluation is presented. Feature evaluation based on class separability criterion is discussed in this paper. A multi-fault SVM classifier based on binary classifier is constructed for bearing faults. Compared with the artificial neural network based method, the SVM based method has desirable advantages. Experiment shows that the algorithm is able to reliably recognize different fault categories. Therefore, it is a promising approach to fault diagnosis of rotating machinery.

Keywords

fault diagnosis; inspection; machine bearings; maintenance engineering; mechanical engineering computing; support vector machines; turbomachinery; artificial neural network based method; class separability criterion; fault diagnosis; feature evaluation; multifault SVM classifier; rolling element bearings fault classification; rotating machinery; support vector machine; Artificial neural networks; Cybernetics; Fault diagnosis; Machine learning; Machinery; Risk management; Rolling bearings; Support vector machine classification; Support vector machines; Voting; Fault diagnosis; Feature evaluation; Support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2009 International Conference on

Conference_Location

Baoding

Print_ISBN

978-1-4244-3702-3

Electronic_ISBN

978-1-4244-3703-0

Type

conf

DOI

10.1109/ICMLC.2009.5212574

Filename

5212574