NDLI: Fault pattern recognition using dynamic independent component based sparse kernel classifier

Content Provider	IEEE Xplore Digital Library
Author	Deng Xiaogang Tian Xuemin
Copyright Year	2012
Description	Author affiliation: College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266555 (Deng Xiaogang; Tian Xuemin)
Abstract	In order to diagnose fault source effectively, this paper proposed a novel fault pattern recognition method called dynamic independent component based sparse kernel classifier (DICSKC). In the proposed method, fault pattern recognition is viewed as a classification problem and kernel trick is applied to construct nonlinear classifier for each fault scene. To improve classification performance, dynamic independent component analysis is used to extract data features which substitute for original measured variables as the input of classifier. For obtaining a sparse classifier to reduce the computation complexity, an orthogonal forward subset selection procedure is utilized to minimize the leave one out classification error. Simulation on the Tennessee Eastman benchmark process shows that the proposed method has a good fault pattern recognition performance.
Starting Page	5322
Ending Page	5327
File Size	315114
Page Count	6
File Format	PDF
ISBN	9781467325813
ISSN	21612927
e-ISBN	9789881563811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	fault pattern recognition Support vector machine classification Training data Feature extraction Brain modeling dynamic independent component analysis Data models Pattern recognition sparse kernel classifier Kernel
Content Type	Text
Resource Type	Article

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