Fault diagnosis for gearbox based on genetic-SVM classifier

Author

Xu, Yunjie ; Li, Wenbin

Author_Institution

Eng. Coll., Beijing Forestry Univ., Beijing, China

Volume

1

fYear

2010

fDate

26-28 Feb. 2010

Firstpage

361

Lastpage

363

Abstract

Failure of gearbox is very complex, so it is difficult to use the mathematical model to describe their faults. In this study, an intelligent diagnostic method based on genetic-support vector machine (GSVM) approach is presented for fault diagnosis of gearbox. The performance of the GSVM system proposed in this study is evaluated by gearbox in the wood-wool working device. The test results show that this GSVM model is effective to detect failure of gearbox in the wood-wool working device.

Keywords

failure (mechanical); fault diagnosis; gears; genetic algorithms; mechanical engineering computing; pattern classification; support vector machines; wood; woodworking machines; wool; GSVM system; failure detection; fault diagnosis; gearbox; genetic-SVM classifier; genetic-support vector machine; intelligent diagnostic method; wood-wool working device; Artificial neural networks; Biological cells; Educational institutions; Fault diagnosis; Forestry; Genetic engineering; Kernel; Risk management; Support vector machine classification; Support vector machines; fault diagnosis; genetic-support vector machine; kernel function parameter; wood-wool working device;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-5585-0

Electronic_ISBN

978-1-4244-5586-7

Type

conf

DOI

10.1109/ICCAE.2010.5451933

Filename

5451933