Efficient shift detection using multivariate exponentially-weighted moving average control charts and principal components

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

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

This paper demonstrates the use of principal components in conjunction with the multivariate exponentially-weighted moving average (MEWMA) control procedure for process monitoring. It is demonstrated that the number of variables to be monitored is reduced through this approach, and that the average run length to detect process shifts or upsets is substantially reduced as well. The performance of the MEWMA applied to all the variables may be related to the MEWMA control chart that uses principal components through the non-centrality parameter. An average run length table demonstrates the advantages of the principal components MEWMA over the procedure that uses all of the variables. An illustrative example is provided.

Original languageEnglish (US)
Pages (from-to)165-171
Number of pages7
JournalQuality and Reliability Engineering International
Volume12
Issue number3
StatePublished - 1996

Fingerprint

Process monitoring
Control charts
Exponentially weighted moving average
Principal components
Average run length

Keywords

  • Average run length
  • Exponentially-weighted moving average
  • Multivariate quality control
  • Statistical process control

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Engineering (miscellaneous)

Cite this

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AB - This paper demonstrates the use of principal components in conjunction with the multivariate exponentially-weighted moving average (MEWMA) control procedure for process monitoring. It is demonstrated that the number of variables to be monitored is reduced through this approach, and that the average run length to detect process shifts or upsets is substantially reduced as well. The performance of the MEWMA applied to all the variables may be related to the MEWMA control chart that uses principal components through the non-centrality parameter. An average run length table demonstrates the advantages of the principal components MEWMA over the procedure that uses all of the variables. An illustrative example is provided.

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