Statistical Monitoring of Dynamic Multivariate Processes: Part II: Identifying Fault Magnitude and Signature

D. Lieftucht, Uwe Kruger, Lei Xie, Timothy Littler, Q. Chen, S.Q. Wang

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

This paper builds on work presented in the first paper, Part 1 [1] and is of equal significance. The paper proposes a novel compensation method to preserve the integrity of step-fault signatures prevalent in various processes that can be masked during the removal of both auto- and cross correlation. Using industrial data, the paper demonstrates the benefit of the proposed method, which is applicable to chemical, electrical, and mechanical process monitoring. This paper, (and Part 1 [1]), has led to further work supported by EPSRC grant GR/S84354/01 involving kernel PCA methods.
Original languageEnglish
Pages (from-to)1677-1688
Number of pages12
JournalIndustrial and Engineering Chemistry Research
Volume45 (5)
Issue number5
DOIs
Publication statusPublished - 01 Feb 2006

ASJC Scopus subject areas

  • Chemical Engineering (miscellaneous)
  • Environmental Science(all)
  • Polymers and Plastics

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