Control charts for monitoring the autocorrelated process parameters: a literature review

In most of the process monitoring, it is assumed that the observations from the process output are independent and identically distributed. But for many processes, the observations are correlated, and when this correlation build-up automatically in the entire process, it is known as autocorrelation. Autocorrelation among the observations can have significant effect on the performance of a control chart. The detection of special cause/s in the process may become very difficult in such situations. Several types of control charts and their combinations are evaluated for their ability to detect changes in the process mean and variance, since two decades. To counter the effect of autocorrelation, various new methodologies and approaches such as double sampling, variable sample sizes and sampling intervals, etc. are suggested by various researchers. Researchers also used Markov chain, time-series approach, MATLAB and artificial neural networks for the simulation of the data. This paper provides a survey and brief summary of the work on the development of the control charts for variables to monitor the mean and dispersion for autocorrelated data.