DocumentCode
1731627
Title
Analog Circuits Fault Diagnosis Based on Support Vector Machine
Author
Yongkui, Sun ; Guangju, Chen ; Hui, Li
Author_Institution
Univ. of Electron. Sci. & Technol. of China, Chengdu
fYear
2007
Abstract
In this paper a new method for diagnosing analog circuits fault based support vector machine (SVM) is presented. The fault features are extracted from the frequency domain response of circuit under test (CUT) and the SVM which trained by the fault features is used to recognize and classify the unknown faults. Support vector machine is simple in architecture and strong generalization ability. The experimental results show that the proposed method for diagnosing analog circuits fault based on SVM correctly classifies faulty components with more than 99% accuracy.
Keywords
analogue circuits; circuit testing; fault diagnosis; support vector machines; analog circuits diagnosis; circuit under test; fault diagnosis; fault features; support vector machine; Analog circuits; Artificial neural networks; Circuit faults; Circuit testing; Fault diagnosis; Feature extraction; Instruments; Neural networks; Support vector machine classification; Support vector machines; analog circuits; fault diagnosis; support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Electronic Measurement and Instruments, 2007. ICEMI '07. 8th International Conference on
Conference_Location
Xi´an
Print_ISBN
978-1-4244-1136-8
Electronic_ISBN
978-1-4244-1136-8
Type
conf
DOI
10.1109/ICEMI.2007.4350996
Filename
4350996
Link To Document