Data-based fault diagnosis of traction converter and simulation study

Author

Wu Chaorong ; Zhao Jin ; Huang Chengguang ; Zhang Jianghan

Author_Institution

Sch. of control Sci. & Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China

fYear

2012

fDate

18-20 July 2012

Firstpage

1512

Lastpage

1516

Abstract

A data-based fault diagnosis method is applied to fault diagnosis of traction converter in this paper. The wavelet transform is used to extract fault characteristics and support vector machine (SVM) is used to diagnose faults. The pros and cons of SVM and radial basis neural network (RBF NN) in fault model classification are also compared follow behind. The simulation results show that, SVM has a good reliability and better generalization capability than RBF NN for fault diagnosis, which verify the superiority of SVM.

Keywords

fault diagnosis; power convertors; power engineering computing; radial basis function networks; support vector machines; traction power supplies; wavelet transforms; data based fault diagnosis; fault model classification; radial basis neural network; support vector machine; traction converter; wavelet transform; Artificial neural networks; Fault diagnosis; Mathematical model; Support vector machines; Training; Wavelet transforms; fault diagnosis; support vector machine (SVM); wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Applications (ICIEA), 2012 7th IEEE Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4577-2118-2

Type

conf

DOI

10.1109/ICIEA.2012.6360963

Filename

6360963