Performance of adaptive exponentially weighted moving average control chart in the presence of measurement error

A control chart is one of the important industrial tools used for monitoring the stability of manufacturing processes. The performance of the control charts can be affected in the presence of measurement error, which also leads to erroneous conclusions. In this article, we examined the effect of measurement error on an adaptive exponentially weighted moving average (AEWMA) control chart by using simple random sampling (SRS) and ranked set sampling (RSS) schemes. The linear covariate model is used to evaluate the control chart in the presence of measurement error. The case of multiple measurements and linear increasing variance is also investigated in the presence of measurement error. The average run length (ARL) and the standard deviation of run length (SDRL) are used as the performance measurement tool. Our results from the simulations and real data application demonstrated that measurement error affects the performance of control charts and multiple measurements are recommended to be used to avoid the effects of measurement error.