An AR-sieve bootstrap control chart for autocorrelated process data

There are two major approaches in dealing with autocorrelated process data in process control, that is, residual-based approaches and methods that modify control limits to adjust for autocorrelation. We proposed a methodology for constructing control charts for autocorrelated process data using the AR-sieve bootstrap. The simulation study illustrates the relative advantage of the AR-sieve bootstrap control chart with respect to the in-control and out-of-control run length and false alarm rate. The proposed methodology works even for small sample sizes and conditions of the near nonstationarity of the generating process. The proposed AR-sieve bootstrap control chart presents the advantage of being distribution-free for certain class of linear models as well as the tracking of actual process observations instead of model residuals, thus facilitating the implementation during actual plant operations.