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

Multivariate statistical process control (SPC) procedures are useful in cas es where several process variables are monitored simultaneously. A signific ant disadvantage of these techniques is that the time required to detect a process shift increases with the number of variables being monitored. We sh ow how the shift detection capability of one popular multivariate SPC schem e, the multivariate analogue of the exponentially weighted moving average c ontrol chart, can be significantly improved by transforming the original pr ocess variables to a lower-dimensional subspace through the use of a U-tran sformation Copyright (C) 1999 John Wiley & Sons, Ltd.