Dynamic concurrent kernel CCA for strip-thickness relevant fault diagnosis of continuous annealing processes

Qiang Liu, Qinqin Zhu, S. Joe Qin*, Tianyou Chai*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

61 Citations (Scopus)

Abstract

The practitioners are concerned with strip-thickness relevant faults of steel-making cold-rolling continuous annealing process (CAP) which is a typical dynamic nonlinear process. In this paper, a novel data-driven dynamic concurrent kernel canonical correlation analysis (DCKCCA) approach is proposed for the diagnosis of the CAP strip thickness relevant faults. First, a DCKCCA algorithm is proposed to capture dynamic nonlinear correlations between strip thickness and process variables. Strip thickness specific variations, process-specific variations, and thickness-process covariations are monitored respectively. Secondly, a multi-block extension of DCKCCA is designed to compute the contributions according to block partition of lagged variables, in order to help localize faults relevant to abnormal strip thickness. Finally, the proposed methods are illustrated by the application to a real continuous annealing process.
Original languageEnglish
Pages (from-to)12-22
JournalJournal of Process Control
Volume67
Online published11 May 2017
DOIs
Publication statusPublished - Jul 2018
Externally publishedYes

Research Keywords

  • Dynamic concurrent kernel canonical correlation analysis
  • Dynamic nonlinear process
  • Fault diagnosis
  • Process modeling
  • Process monitoring

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