MONITORING CLEAN ROOM AIR USING CUSCORE CHARTS

In many process monitoring situations where the data are collected ove r time, the measurements tend to be autocorrelated. In those cases one can fit a time series model to the data to account for the autocorrel ation. Once a model has been fitted, it can be used to predict the beh aviour of the process, and the residuals can be used to monitor the pr ocess using e.g., a Shewhart or Cusum chart. Two issues that are often overlooked when fitting time series models and using the residuals fo r process monitoring are (i) the validity of the underlying assumption that the time series model fitted to the data will also fit future ob servations reasonably well, and (ii) that its parameters will remain f airly constant. Either of these assumptions, or both, might not hold, which in turn will affect the performance of the monitoring scheme. In this paper we show how Cuscore charts can be used to check these assu mptions by detecting changes in the parameters of an integrated moving average (IMA) model used to monitor the air quality in a clean room e nvironment. The Cuscore can also be effectively used to detect non-sta tionarity in time series data as well as departures from a state of st atistical control. (C)1998 John Wiley & Sons, Ltd.