DocumentCode :
672986
Title :
Analog Circuit Fault Diagnosis Based on Wavelet Kernel Support Vector Machine
Author :
Ke Guo ; Sheling Wang ; Jiahong Song
Author_Institution :
Beijing Inst. of Space Long March Vehicle, Beijing, China
fYear :
2013
fDate :
16-17 Nov. 2013
Firstpage :
395
Lastpage :
399
Abstract :
Analog circuit fault diagnosis can be regarded as the pattern recognition issue and addressed by machine learning theory. As compared with neural networks, support Vector Machine (SVM) is based on statistical learning theory, which has advantages of better classification ability and generalization performance. The marr wavelet kernel is proposed and the existence is proven by theoretic analysis and demonstration. Based on this, a novel analog circuit fault diagnosis method which is called wavelet kernel support vector machine is proposed in the paper. Using principal component analysis (PCA) as a tool for extracting fault features, the WSVM is then applied to the analog circuit fault diagnosis. The effectiveness of the proposed method is verified by the experimental results.
Keywords :
analogue circuits; circuit analysis computing; fault diagnosis; feature extraction; learning (artificial intelligence); pattern classification; principal component analysis; support vector machines; wavelet transforms; PCA; WSVM; analog circuit fault diagnosis; classification ability; fault feature extraction; generalization performance; machine learning theory; marr wavelet kernel; neural networks; pattern recognition issue; principal component analysis; statistical learning theory; wavelet kernel support vector machine; Analog circuits; Circuit faults; Fault diagnosis; Feature extraction; Kernel; Principal component analysis; Support vector machines; analog circuit; fault diagnosis; support vector machine; wavelet kernel;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology and Applications (ITA), 2013 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4799-2876-7
Type :
conf
DOI :
10.1109/ITA.2013.97
Filename :
6710013
Link To Document :
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