NDLI: A new optimization identification method for fault diagnosis

Content Provider	IEEE Xplore Digital Library
Author	WenJie Tian JiCheng Liu
Copyright Year	2010
Description	Author affiliation: Automation Institute, Beijing Union University, China (WenJie Tian; JiCheng Liu)
Abstract	Fault diagnosis of electronic circuit is important for safety of the device and relevant power system. In the study, support vector regression (SVR) classifiers combined with the particle swarm optimization algorithm (POSA) are applied to construct diagnostic model of electronic circuit, and the diagnostic system structure of electronic circuit is presented on the basis of the model. It is powerful for the practical problem with small sampling, nonlinear and high dimension, which is very suitable for online fault diagnosis. Utilizing the character that principal components analysis algorithm can keep the discernability of original dataset after reduction, reduce of the original dataset is calculated and used to train individual SVR for ensemble, and consequently, increase the detection accuracy. The test results show that the proposed method is a promised method owning to its high diversity, high detection accuracy and faster speed in fault diagnosis. The experimental result shows that this fault detection method is feasible and effective.
Starting Page	13
Ending Page	16
File Size	672456
Page Count	4
File Format	PDF
ISBN	9781424451920
ISSN	19483422
DOI	10.1109/CAR.2010.5456752
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	Safety devices Optimization methods Power system modeling Power system faults Particle swarm optimization principal components analysis Fault diagnosis support vector regression fault Diagnosis Fault detection Sampling methods reduction particle swarm optimization algorithm Electronic circuits Principal component analysis
Content Type	Text
Resource Type	Article

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