Application of SVM to engine parameter collector fault diagnosis

Author

Qin Bo ; Chen Ming ; Zhang Hao

Author_Institution

Coll. of Autom., Northwestern Polytech. Univ., Xi´an

fYear

2008

fDate

10-12 Dec. 2008

Firstpage

1

Lastpage

4

Abstract

Support Vector Machine (SVM), based on structural risk minimization principle, is now widely used in pattern recognition, classification and other research fields. It shows better generalization performance than traditional statistical learning theory, especially in small samples. In this paper, some dimensionless parameter is selected as SVM eigenvector, and then support vector machine is applied to fault diagnosis in engine parameter collector. Result shows that it has good ability in fault pattern classification of engine parameter collector.

Keywords

eigenvalues and eigenfunctions; engines; fault diagnosis; mechanical engineering computing; pattern classification; risk analysis; support vector machines; SVM; eigenvector; engine parameter collector; fault diagnosis; fault pattern classification; pattern recognition; statistical learning theory; structural risk minimization; support vector machine; Condition monitoring; Engines; Fault diagnosis; Fuels; Hydrogen; Machine learning; Risk management; Statistical learning; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems and Control in Aerospace and Astronautics, 2008. ISSCAA 2008. 2nd International Symposium on

Conference_Location

Shenzhen

Print_ISBN

978-1-4244-3908-9

Electronic_ISBN

978-1-4244-2386-6

Type

conf

DOI

10.1109/ISSCAA.2008.4776259

Filename

4776259