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Improving the performance of the multivariate exponentially weighted moving average control chart
Author(s) -
Runger George C.,
Keats J. Bert,
Montgomery Douglas C.,
Scranton Richard D.
Publication year - 1999
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/(sici)1099-1638(199905/06)15:3<161::aid-qre215>3.0.co;2-v
Subject(s) - multivariate statistics , control chart , statistical process control , ewma chart , statistics , subspace topology , chart , mathematics , multivariate analysis , transformation (genetics) , x bar chart , process (computing) , computer science , artificial intelligence , biochemistry , chemistry , gene , operating system
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. Copyright © 1999 John Wiley & Sons, Ltd.