Improving the performance of the multivariate exponentially weighted moving average control chart

George Runger, J. Bert Keats, Douglas Montgomery, Richard D. Scranton

Research output: Contribution to journalArticle

21 Scopus citations

Abstract

Multivariate statistical process control (SPC) procedures are useful in cases where several process variables are monitored simultaneously. A significant disadvantage of these techniques is that the time required to detect a process shift increases with the number of variables being monitored. We show how the shift detection capability of one popular multivariate SPC scheme, the, multivariate analogue of the exponentially weighted moving average control chart, can be significantly improved by transforming the original process variables to a lower-dimensional subspace through the use of a U-transformation.

Original languageEnglish (US)
Pages (from-to)161-166
Number of pages6
JournalQuality and Reliability Engineering International
Volume15
Issue number3
DOIs
StatePublished - Jan 1 1999

Keywords

  • EWMA
  • Multivariate analysis
  • Principal component analysis
  • Process monitoring
  • SPC

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research

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