Detection and Classification of Rolling-Element Bearing Faults using Support Vector Machines

Author

Rojas, Alfonso ; Nandi, Asoke K.

Author_Institution

Dept. of Electr. Eng. & Electron., Liverpool Univ.

fYear

2005

fDate

28-28 Sept. 2005

Firstpage

153

Lastpage

158

Abstract

This paper proposes development of support vector machines (SVMs) for detection and classification of rolling-element bearing faults. The training of the SVMs is carried out using the sequential minimal optimization (SMO) algorithm. In this paper, a mechanism for selecting adequate training parameters is proposed. This proposal makes the classification procedure fast and effective. Various scenarios are examined using two sets of vibration data, and the results are compared with those available in the literature that are relevant to this investigation

Keywords

fault diagnosis; learning (artificial intelligence); mechanical engineering computing; optimisation; pattern classification; rolling bearings; support vector machines; fault classification; fault detection; rolling-element bearing faults; sequential minimal optimization; support vector machines; vibration data; Electrical fault detection; Fault detection; Inspection; Machinery; Proposals; Rolling bearings; Signal processing; Signal processing algorithms; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning for Signal Processing, 2005 IEEE Workshop on

Conference_Location

Mystic, CT

Print_ISBN

0-7803-9517-4

Type

conf

DOI

10.1109/MLSP.2005.1532891

Filename

1532891