Control charts are a type of statistical tool used to control a production process in order to obtain the quality products that can fulfill the demands of both the manufacturer and the consumers. In this paper, we propose the Tukey Moving Average-Exponentially Weighted Moving Average control chart (MME-TCC) to detect the change of average of the process with symmetric and asymmetric distribution and to compare the efficiency in detecting the change of the MME-TCC to the MA, MME, MEM, MA-TCC and MEM-TCC at the various change levels of the parameter. The criteria to measure the efficiency were average run length (ARL), standard deviation of run length (SDRL), and median run length (MRL) which evaluated by using Monte Carlo simulation (MC), The research results showed that the proposed control chart has the highest efficiency in detecting the change when the change level was at $-0.75\le \delta \le 0.75$ . However, if the change of parameter increased ( $\delta \ge 1.00$ ), the MME had more efficiency. In the case where the observation was logistic distributions, the MA-TCC had more efficiency to detect the change. Moreover, from applying the proposed control chart to two sets of real data, the mine explosion period in the UK during 1875–1951 and data of diameter of the workpiece from an industrial factory, it was found that the MME-TCC was able to more quickly detect the change than the other control charts.

中文翻译：

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用于检测均值漂移的新非参数 Tukey MA-EWMA 控制图

控制图是一种统计工具，用于控制生产过程，以获得能够满足制造商和消费者需求的优质产品。在本文中，我们提出了 Tukey 移动平均-指数加权移动平均控制图 (MME-TCC) 来检测对称和非对称分布过程的平均值变化，并比较检测 MME-TCC 变化的效率到参数的各种变化级别的 MA、MME、MEM、MA-TCC 和 MEM-TCC。衡量效率的标准是平均游程长度（ARL）、游程长度标准差（SDRL）和中位游程长度（MRL），它们通过蒙特卡罗模拟（MC）进行评估，研究结果表明，当变化水平在$-0.75\le\delta\le 0.75$时，所提出的控制图检测变化的效率最高。但是，如果参数的变化增加（ $\delta \ge 1.00$ ），则 MME 的效率更高。在观察是逻辑分布的情况下，MA-TCC 检测变化的效率更高。此外，将所提出的控制图应用于两组真实数据，即英国 1875-1951 年的矿山爆炸时期和工业工厂的工件直径数据，发现 MME-TCC 能够更多地比其他控制图更快地检测到变化。在观察是逻辑分布的情况下，MA-TCC 检测变化的效率更高。此外，将所提出的控制图应用于两组真实数据，即英国 1875-1951 年的矿山爆炸时期和工业工厂的工件直径数据，发现 MME-TCC 能够更多地比其他控制图更快地检测到变化。在观察是逻辑分布的情况下，MA-TCC 检测变化的效率更高。此外，将所提出的控制图应用于两组真实数据，即英国 1875-1951 年的矿山爆炸时期和工业工厂的工件直径数据，发现 MME-TCC 能够更多地比其他控制图更快地检测到变化。