NDLI: Fault diagnosis for power circuits based on SVM within the Bayesian framework

Content Provider	IEEE Xplore Digital Library
Author	Binyuan Ye Zhiyong Luo Wenfeng Zhang Changhao Piao
Copyright Year	2008
Description	Author affiliation: Dept. of Mech.&Electr. Eng., Guangdong Vocational Coll. of Mech.&Electr. Technol., Guangzhou (Binyuan Ye)
Abstract	Based on least squares wavelet support vector machines (LS-WSVM) within the Bayesian evidence framework, a systematic method for fault diagnosis of power circuits is presented. In this paper, the Bayesian evidence framework is applied to select the optimal values of the regularization and kernel parameters of multi-class LS-WSVM classifiers. Also wavelet coefficients of output voltage signals of power circuits under faulty conditions are obtained with wavelet lifting decomposition, and then faulty feature vectors are extracted from the disposed wavelet coefficients. The faulty feature vectors are used to train the multi-class LS-WSVM classifiers, so the model of the power circuits fault diagnosis system is built. In push-pull circuits, this method is applied to diagnose the faults of the circuits with simulation; the results show that the fault diagnosis method of the power circuits with LS-WSVM within the Bayesian evidence framework is effective.
Starting Page	5125
Ending Page	5129
File Size	226314
Page Count	5
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4593762
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Circuit faults Kernel Bayesian methods Support vector machines Fault diagnosis Support vector machine classification Feature extraction fault diagnosis LS-WSVM Bayesian evidence framework wavelet lifting decomposition power circuits
Content Type	Text
Resource Type	Article

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