NDLI: Decentralized Fault Diagnosis of Continuous Annealing Processes Based on Multilevel PCA

Content Provider	IEEE Xplore Digital Library
Author	Qiang Liu Qin, S.J. Tianyou Chai
Copyright Year	2004
Abstract	Process monitoring and fault diagnosis of the continuous annealing process lines (CAPLs) have been a primary concern in industry. Stable operation of the line is essential to final product quality and continuous processing of the upstream and downstream materials. In this paper, a multilevel principal component analysis (MLPCA)-based fault diagnosis method is proposed to provide meaningful monitoring of the underlying process and help diagnose faults. First, multiblock consensus principal component analysis (CPCA) is extended to MLPCA to model the large scale continuous annealing process. Secondly, a decentralized fault diagnosis approach is designed based on the proposed MLPCA algorithm. Finally, experiment results on an industrial CAPL are obtained to demonstrate the effectiveness of the proposed method.
Sponsorship	IEEE Robotics and Automation Society
Starting Page	687
Ending Page	698
Page Count	12
File Size	2624565
File Format	PDF
ISSN	15455955
Volume Number	10
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Loading Fault diagnosis Monitoring Vectors Strips Annealing process monitoring industrial processes principal component analysis (PCA)
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering

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