Improved Principal Component Monitoring of Large-Scale Processes

Uwe Kruger, George Irwin, Y. Zhou

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)


This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.
Original languageEnglish
Pages (from-to)879-888
Number of pages10
JournalJournal of Process Control
Issue number8
Publication statusPublished - Mar 2004

ASJC Scopus subject areas

  • Process Chemistry and Technology
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering


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