Roller bearings fault diagnosis based on LS-SVM

Author

Sui, Wentao ; Zhang, Dan ; Wang, Wilson

Author_Institution

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

fYear

2009

fDate

5-7 Aug. 2009

Firstpage

1848

Lastpage

1851

Abstract

A new method of roller bearings fault diagnosis based on least squares support vector machines (LS-SVM) was presented. Feature selection method based on simulated annealing (SA) algorithm was discussed in this paper. LS-SVM classifier was constructed for bearing faults. Compared with the Artificial Neural Network based method, the LS-SVM based method possessed desirable advantages. Experiment shows that the presented method is able to reliably recognize different fault categories.

Keywords

fault diagnosis; feature extraction; least squares approximations; mechanical engineering computing; pattern classification; rolling bearings; simulated annealing; support vector machines; LS-SVM classifier; feature selection method; least squares support vector machine; roller bearing fault diagnosis; simulated annealing algorithm; Artificial intelligence; Artificial neural networks; Chemical industry; Fault diagnosis; Least squares methods; Risk management; Rolling bearings; Signal processing algorithms; Support vector machine classification; Support vector machines; fault diagnosis; feature evaluation; signal processing; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation and Logistics, 2009. ICAL '09. IEEE International Conference on

Conference_Location

Shenyang

Print_ISBN

978-1-4244-4794-7

Electronic_ISBN

978-1-4244-4795-4

Type

conf

DOI

10.1109/ICAL.2009.5262645

Filename

5262645