Analog Circuit Fault Diagnosis Using AdaBoost with SVM-Based Component Classiﬁers

Bao Yu Dong; Guang Ren

doi:10.4028/www.scientific.net/AMR.591-593.1414

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 591-593Analog Circuit Fault Diagnosis Using AdaBoost with...

Analog Circuit Fault Diagnosis Using AdaBoost with SVM-Based Component Classiﬁers

Abstract:

This paper presents a novel method of analog circuit fault diagnosis using AdaBoost with SVM-based component classiﬁers. We use binary-SVMs of o-a-r SVM as weak classifiers and design appropriate structure of SVM ensemble. Tent map is used to adjust parameters of SVM component classifiers for maintaining the diversity of weak classifiers. In simulation experiment, we use Monte-carlo analysis for 40kHz Sallen-Key bandpass ﬁlter and get transient response of thirteen faults. We extract feature vector by db3 wavelet packet transform and principal component analysis (PCA), and diagnose circuit faults by different methods. Simulation results show that the proposed method has the higher classification accuracy compared with other SVM methods. The generalization performance of ensemble method is good. It is suitable for practical use

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Periodical:

Advanced Materials Research (Volumes 591-593)

Pages:

1414-1417

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.591-593.1414

Citation:

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Online since:

November 2012

Authors:

Bao Yu Dong, Guang Ren

Keywords:

Adaboost, Analog Circuit, Fault Diagnosis, Support Vector Machine (SVM)

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References

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