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

94 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

Bibliographical note

This paper was recommended for publication by Associate Editor H. Darabi and Editor Y. Narahari upon evaluation of the reviewers' comments.

Funding

This work was supported in part by the National Basic Research Program of China (973 Program) under Grant 2009CB320601, the 111 Project of Ministry of Education of China under Grant B08015, the Natural Science Foundation of China under Grant 61020106003, Grant 61290323, and Grant 61104084, the State Administration of Foreign Experts Affairs of China's Special Program for Elite Overseas Experts (for S. Joe Qin), and funds for Creative Research Groups of China under Grant 60821063.

Keywords

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

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