Study of fault diagnosis model based on multi-class wavelet support vector machines

Author

Peng Lu ; Xu, Da-ping ; Liu, Yi-bing

Author_Institution

Sch. of Math. & Phys., North China Electr. Power Univ., Beijing, China

Volume

7

fYear

2005

fDate

18-21 Aug. 2005

Firstpage

4319

Abstract

Compromising wavelet kernel and multi-class least squares support vector machines, this article put forward to a fault diagnosis model based on multi-class wavelet support vector machine. The model heightens auto-adaptive model classification ability by adjustment of scale parameter of wavelet kernel, and make use of remarkable learning ability and generalization ability of small sample of vector machines to improve speed and effectiveness of fault diagnosis. And taking fault diagnosis in heat-recycling system of thermal power plant as an example to analysis.

Keywords

adaptive systems; fault diagnosis; generalisation (artificial intelligence); heat systems; learning (artificial intelligence); least squares approximations; pattern classification; support vector machines; thermal power stations; wavelet transforms; autoadaptive model classification; fault diagnosis; generalization; heat recycling system; learning; multiclass least squares support vector machine; multiclass wavelet support vector machines; thermal power plant; wavelet kernel; Automation; Fault diagnosis; Kernel; Least squares methods; Machine learning; Mathematical model; Power system modeling; Support vector machine classification; Support vector machines; Wavelet analysis; fault diagnosis; heat-recycling system; multi-class least squares support vector machines; wavelet kernel;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on

Conference_Location

Guangzhou, China

Print_ISBN

0-7803-9091-1

Type

conf

DOI

10.1109/ICMLC.2005.1527697

Filename

1527697