The adaptive EWMA (AEWMA) chart provides better sensitivity than the EWMA chart when detecting mean shifts that lie within a specific interval. In this paper, we propose a novel AEWMA chart for monitoring the mean of a normally distributed process. The proposed AEWMA chart is parameter-free apart from its decision interval, which makes it very easy to implement, and at the same time, it provides balanced protection against mean shifts of various magnitudes. The idea is to estimate the mean shift using a Shewhart statistic, and then adaptively select a suitable smoothing constant for the EWMA chart based on the estimated mean shift size. The Monte Carlo simulation method is used to compute the zero-state and steady-state run length characteristics. Based on detailed run length comparisons, it is found that the proposed AEWMA chart outperforms the existing AEWMA charts when detecting small, moderate and large shifts simultaneously in the process mean. A real data application is provided to support the theory.

当检测到特定时间间隔内的均值漂移时，自适应EWMA（AEWMA）图表比EWMA图表提供更好的灵敏度。在本文中，我们提出了一种新颖的AEWMA图表，用于监控正态分布过程的平均值。所提出的AEWMA图表除其决策间隔外没有参数，这使其易于实现，同时，它还提供了针对各种幅度的均值漂移的平衡保护。这个想法是使用Shewhart统计量来估计平均漂移，然后根据估计的平均漂移大小为EWMA图自适应地选择合适的平滑常数。蒙特卡罗模拟方法用于计算零状态和稳态游程长度特性。根据详细的行程比较，发现在同时检测过程平均值中的小，中，大偏移时，建议的AEWMA图表优于现有的AEWMA图表。提供了一个实际的数据应用程序来支持该理论。