Control charts are widely known quality tools used to detect and control industrial process deviations in statistical process control. In the current paper, we propose a new single memory-type control chart, called the sum of squares triple exponentially weighted moving average control chart (referred as SS-TEWMA chart), that simultaneously detects shifts in the process mean and/or process dispersion. The run length performance of the proposed SS-TEWMA control chart is compared with that of the sum of squares EWMA, sum of squares double EWMA, sum of squares generally weighted moving average, and sum of squares double generally weighted moving average, control charts, through Monte Carlo simulations. The comparisons indicate that the proposed chart is more efficient, than the competing ones, in detecting small shifts in the process mean and/or variability for most of the considered scenarios, while it has comparable performance for some others in identifying large shifts in the process mean and small to large shifts in the process variability. Finally, two illustrative examples are provided to explain the application of the SS-TEWMA control chart.

中文翻译：

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平方和三重指数加权移动平均控制图

控制图是众所周知的质量工具，用于检测和控制统计过程控制中的工业过程偏差。在当前的论文中，我们提出了一种新的单一记忆型控制图，称为平方和三重指数加权移动平均控制图（称为 SS-TEWMA 图），它同时检测过程均值和/或过程离散度的变化. 将所提出的 SS-TEWMA 控制图的运行长度性能与平方和 EWMA、平方和双 EWMA、平方和一般加权移动平均和平方和双一般加权移动平均、控制图、通过蒙特卡罗模拟。比较表明，提议的图表比竞争图表更有效，在检测大多数考虑的场景的过程平均值和/或变异性的小变化时，同时它在识别过程平均值的大变化和过程变异性的小到大变化方面具有与其他一些类似的性能。最后，提供了两个说明性示例来解释 SS-TEWMA 控制图的应用。