Decentralized Fault Diagnosis of Continuous Annealing Processes Based on Multilevel PCA

Qiang LIU*, S. Joe QIN*, Tianyou CHAI*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

87 Citations (Scopus)

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, amultilevel 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. © 2012 IEEE.
Original languageEnglish
Pages (from-to)687-698
Number of pages12
JournalIEEE Transactions on Automation Science and Engineering
Volume10
Issue number3
Early online date24 Jan 2013
DOIs
Publication statusPublished - Jul 2013
Externally publishedYes

Keywords

  • Fault diagnosis
  • Industrial processes
  • Principal component analysis (PCA)
  • Process monitoring

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