Decentralized fault diagnosis of large-scale processes using multiblock kernel partial least squares

Yingwei ZHANG*, Hong ZHOU, S. Joe QIN, Tianyou CHAI

*Corresponding author for this work

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

265 Citations (Scopus)

Abstract

In this paper, a decentralized fault diagnosis approach of complex processes is proposed based on multiblock kernel partial least squares (MBKPLS). To solve the problem posed by nonlinear characteristics, kernel partial least squares (KPLS) approaches have been proposed. In this paper, MBKPLS algorithm is first proposed and applied to monitor large-scale processes. The advantages of MBKPLS are: 1) MBKPLS can capture more useful information between and within blocks compared to partial least squares (PLS); 2) MBKPLS gives nonlinear interpretation compared to MBPLS; 3) Fault diagnosis becomes possible if number of sub-blocks is equal to the number of the variables compared to KPLS. The proposed methods are applied to process monitoring of a continuous annealing process. Application results indicate that the proposed decentralized monitoring scheme effectively captures the complex relations in the process and improves the diagnosis ability tremendously. © 2009 IEEE.
Original languageEnglish
Article number5340619
Pages (from-to)3-10
Number of pages8
JournalIEEE Transactions on Industrial Informatics
Volume6
Issue number1
Early online date24 Nov 2009
DOIs
Publication statusPublished - Feb 2010
Externally publishedYes

Bibliographical note

This work was supported in part by China’s National 973 Program (2009CB320600) and the 111 Project. Paper no. TII-08-12-0234.R2.

Keywords

  • Fault diagnosis
  • Multiblock kernel partial least squares (MBKPLS)
  • Nonlinear component analysis
  • Process monitoring.

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