Research and Application of a Hierarchical Fault Diagnosis System Based on Support Vector Machine

Author

Liu, Ailun ; Yuan, Xiaoyan ; Yu, Jinshou

Author_Institution

East China Univ. of Sci. & Technol., Shanghai

Volume

2

fYear

2007

Firstpage

59

Lastpage

65

Abstract

support vector machine (SVM) is a kind of machine learning method based on the statistical learning theory, it has been applied in the fault diagnosis field. After analyzing SVM pattern classification theory, a hierarchical structure fault detection and identification (FDI) system is presented in this paper, and simulation results show that this method can effectively handle the complex process characteristic and improve FDI model performance.

Keywords

fault diagnosis; learning (artificial intelligence); pattern classification; support vector machines; fault detection system; fault identification system; hierarchical fault diagnosis system; hierarchical structure; machine learning; pattern classification theory; statistical learning theory; support vector machine; Analytical models; Fault detection; Fault diagnosis; Learning systems; Pattern analysis; Pattern classification; Performance analysis; Statistical learning; Support vector machine classification; Support vector machines; Support Vector Machine (SVM); diagnosis; fault; pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2007. ICNC 2007. Third International Conference on

Conference_Location

Haikou

Print_ISBN

978-0-7695-2875-5

Type

conf

DOI

10.1109/ICNC.2007.607

Filename

4344316