NDLI: Fault diagnosis model research based on support vector regression and principal components analysis

Content Provider	IEEE Xplore Digital Library
Author	WenJie Tian JiCheng Liu
Copyright Year	2010
Description	Author affiliation: Beijing Automation Institute of Beijing Union University, China, 100101 (WenJie Tian; JiCheng Liu)
Abstract	To overcome the deficiencies of low accuracy and high false alarm rate in fault diagnosis system, a new optimization method for f the fault diagnosis model is proposed based on support vector regression (SVR) and principal components analysis. Utilizing the character that principal components analysis algorithm can keep the discernability of original dataset after reduction, 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 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	3896
Ending Page	3899
File Size	182638
Page Count	4
File Format	PDF
ISBN	9781424451814
DOI	10.1109/CCDC.2010.5498471
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parameter estimation Automation Fault Diagnosis Artificial neural networks Support Vector Regression Diagnostic expert systems Fault diagnosis Home appliances Reduction Fault detection Ensemble State estimation Electronic circuits Principal component analysis Principal Components Analysis
Content Type	Text
Resource Type	Article

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