NDLI: A novel fault diagnosis model research for electronic circuit

Content Provider	IEEE Xplore Digital Library
Author	JiCheng Liu WenJie Tian
Copyright Year	2010
Description	Author affiliation: Automation Institute, Beijing Union University, China (JiCheng Liu; WenJie Tian)
Abstract	Support vector machine (SVM) which overcomes the drawbacks of neural networks has been widely used for pattern recognition in recent years. A new optimization method for the fault diagnosis model is proposed. To overcome the deficiencies of low accuracy and high false alarm rate in fault diagnosis system, an integrated fault diagnosis model based on support vector regression and principal components analysis is proposed in the paper. Utilizing the character that principal components analysis algorithm, the reduces of the original dataset are calculated and used to train individual SVR classifier for ensemble, which increase the diversity between individual classifiers, and consequently, increase the detection accuracy. To validate the effectiveness of the proposed method, simulation experiments are performed based on the electronic circuit dataset. The real electronic circuit data sets are used to investigate its feasibility in fault diagnosis. The results show that the proposed method is a promised ensemble method owning to its high diversity, high detection accuracy and faster speed in fault diagnosis.
Starting Page	5
Ending Page	8
File Size	565062
Page Count	4
File Format	PDF
ISBN	9781424451920
ISSN	19483422
DOI	10.1109/CAR.2010.5456606
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	fault diagnosis Circuit simulation Optimization methods Electrical fault detection ensemble Pattern recognition principal components analysis Fault diagnosis Support vector machines support vector regression Fault detection Neural networks reduction Electronic circuits Principal component analysis
Content Type	Text
Resource Type	Article

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