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.

指数加权移动平均 (EWMA) 和累积总和 (CUSUM) 等记忆型控制图设计用于检测生产过程中的小/中度变化。自适应 EWMA (AEWMA) 和自适应 CUSUM 控制图因其出色的换档检测速度而备受关注。在本文中，我们提出了排序集合抽样、配对排序集合抽样、极端配对排序集合和四分位配对排序抽样下的AEWMA控制图，用于监测过程均值的不频繁变化。基于广泛的模拟，计算平均运行长度分布。结果表明，所提出的控制图比本研究中考虑的控制图具有更好的换档诊断能力。