NDLI: Subspace decomposition approach of fault deviations and its application to fault reconstruction

Content Provider	IEEE Xplore Digital Library
Author	Chunhui Zhao Wenqing Li Youxian Sun
Copyright Year	2013
Description	Author affiliation: Dept. of Control Sci. & Eng., Zhejiang Univ., Hangzhou, China (Chunhui Zhao; Wenqing Li; Youxian Sun)
Abstract	In the present work, a new subspace decomposition approach of fault deviation is developed for fault diagnosis via reconstruction for principle component analysis (PCA) based monitoring system. It focuses on decomposing the fault effects in different monitoring subspaces, principal subspace (PCS) and residual subspace (RS), and finding the significant fault deviations that are responsible for the concerned alarming monitoring statistic. This is achieved by analyzing the relative changes of fault process in comparison with the normal status and designing a two-step feature decomposition procedure. Each fault data space is decomposed into two different parts for the purpose of fault reconstruction in PCS and RS of monitoring system respectively. One is composed of the concerned fault deviations that contribute to alarming monitoring statistics which are thus significant to remove the out-of-control signals. The other is composed of general variations that are deemed to follow normal rules or insignificant to remove alarming monitoring statistics. Its feasibility and performance are illustrated with experimental data.
Starting Page	6122
Ending Page	6127
File Size	146547
Page Count	6
File Format	PDF
ISBN	9789881563835
ISSN	19341768
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	fault reconstruction fault diagnosis principal component subspace (PCS) residual subspace (RS) Process control Vectors Fault diagnosis subspace decomposition Analytical models Data models Monitoring principal component analysis Principal component analysis
Content Type	Text
Resource Type	Article

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