DocumentCode :
676729
Title :
A new analog circuit fault diagnosis approach based on GA-SVM
Author :
Shaowei Chen ; Shuai Zhao ; Cong Wang
Author_Institution :
Sch. of Electron. & Inf., Northwestern Polytech. Univ., Xi´an, China
fYear :
2013
fDate :
22-25 Oct. 2013
Firstpage :
1
Lastpage :
4
Abstract :
Fault diagnosis is crucial for analog circuits. In this paper, a new fault diagnosis method based on genetic algorithm and support vector machine (GA-SVM) is proposed. We design fault mode and collect the fault datasets on the basis of a quad high pass filter circuit. Wavelet packet analysis is employed to extract fault samples information. Sampled data´s dimension is further reduced by Principal Component Analysis(PCA). To improve the efficiency of SVM, we use GA to search optimized parameters for its kernel function. After being trained with sampled data, the optimized SVM can steadily classify circuit faults. Simulation results show that the new algorithm classifies circuit faults at an accuracy of 92.69%. Our approach provides a new direction for analog circuit fault diagnosis.
Keywords :
analogue integrated circuits; fault diagnosis; genetic algorithms; high-pass filters; principal component analysis; support vector machines; GA-SVM; PCA; analog circuit; circuit faults; fault diagnosis; genetic algorithm; kernel function; principal component analysis; quad high pass filter; support vector machine; wavelet packet analysis; Circuit faults; Fault diagnosis; Genetic algorithms; Kernel; Support vector machines; Testing; Training; Analog Circuits; GA; PCA; SVM; Wavelet;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
TENCON 2013 - 2013 IEEE Region 10 Conference (31194)
Conference_Location :
Xi´an
ISSN :
2159-3442
Print_ISBN :
978-1-4799-2825-5
Type :
conf
DOI :
10.1109/TENCON.2013.6718926
Filename :
6718926
Link To Document :
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