Adaptive Exponentially Weighted Moving Average Control Chart for Monitoring Process Mean Under Ranked Set Sampling Schemes

The memory-type control charts such as exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) are designed to be optimal for detecting small/moderate shifts in the production process. The adaptive EWMA (AEWMA) and adaptive CUSUM control charts have gained considerable attention because of their excellent speed to shift detection. In this paper, we proposed AEWMA control charts under ranked set sampling, paired ranked set sampling, extreme paired ranked set and quartile paired ranked sampling for monitoring the infrequent changes in the process mean. Based on extensive simulations, the average run length profiles are computed. It is revealed that the proposed control chart has better shift diagnostic abilities than the control charts considered in this study. An example of real data is also presented to demonstrate the behavior and implementation of the proposed AEWMA control charts.