NDLI: Fault diagnosis method based on multiple sparse kernel classifiers

Content Provider	IEEE Xplore Digital Library
Author	Xiaogang Deng Xuemin Tian
Copyright Year	2011
Description	Author affiliation: College of Information & Control Engineering, China University of Petroleum, Dongying, China (Xiaogang Deng; Xuemin Tian)
Abstract	Nonlinear fault diagnosis methods based on kernel function have great computation complexity for all training samples are introduced in model training. This paper proposes a novel nonlinear fault diagnosis method based on multiple sparse kernel classifiers (MSKC). In the proposed method, fault diagnosis is viewed as a nonlinear classification problem between normal data and fault data. Kernel trick is applied to construct multiple nonlinear classifiers for different fault scenes. In order to reduce the complexity of kernel classifier and improve classifier generalization capability, a forward orthogonal selection procedure is applied to minimize the leave one out classification error. Lastly, multiple sparse kernel classifiers are combined by weight voting technique to build a monitoring statistic. Simulation of a continuous stirred tank reactor system shows that the proposed method performs better compared with kernel principal component analysis method in terms of fault detection performance and computation efficiency.
Starting Page	213
Ending Page	218
File Size	172656
Page Count	6
File Format	PDF
ISBN	9781612843742
e-ISBN	9781612843759
DOI	10.1109/IWACI.2011.6160005
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Computational modeling Data models Mathematical model Kernel Monitoring Principal component analysis
Content Type	Text
Resource Type	Article

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