Research on Fault Diagnosis Based on Wavelet Packet Multi-class Classification SVM

Author

Xiaogang Xu ; Songling Wang ; Fei Li ; Zhengren Wu ; Wei Sun

Author_Institution

Dept. of Power Eng., North China Electr. Power Univ., Baoding, China

fYear

2010

fDate

13-15 Dec. 2010

Firstpage

164

Lastpage

167

Abstract

It\´s still in search that how to apply SVM in muti-classify. Directed Acyclic Graph is easier to be computed and has better learning effect than other arithmetic. Experimental platform is used to simulate typical faults of circumrotate machines. Based on the frequency domain feature, energy eigenvector of frequency domain is presented using wavelet packet analysis method. DAGSVM is applied in classification, a "grid-search" is applied on C and using cross-validation. The classify effect is more veracious that of BP network.

Keywords

directed graphs; fault diagnosis; learning (artificial intelligence); pattern classification; search problems; support vector machines; SVM; circumrotate machine; cross validation method; directed acyclic graph; energy eigenvector; fault diagnosis; fault simulation; frequency domain feature; grid search; learning effect; multiclass classification; wavelet packet analysis; DAGSVM; fault diagnosis; pattern matching; rotating machine; wavelet packet;

fLanguage

English

Publisher

ieee

Conference_Titel

Manufacturing Automation (ICMA), 2010 International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4244-9018-9

Electronic_ISBN

978-0-7695-4293-5

Type

conf

DOI

10.1109/ICMA.2010.41

Filename

5695173