Monitoring the autocorrelated process mean and variance using a GWMA control chart

The main problem addressed in this study is to monitor small shifts in the process mean and/or variance for which observational data meet significant autocorrelation. A generally weighted moving average (GWMA) control chart for monitoring a process is introduced in which the observations can be modeled as a first-order autoregressive process with a random error. Using simulation, the average run lengths (ARLs) of control schemes are compared. The results are showed that the GWMA control chart of observations requires less time to detect small process mean and/or variance than EWMA control chart.