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A mixed GWMA-CUSUM control chart for monitoring the process mean

机译:GWMA-CUSUM混合控制图,用于监控过程平均值

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The memory-type control charts are widely used in the process and service industries for monitoring the production processes. The reason is their sensitivity to quickly react against the small process disturbances. Recently, a new cumulative sum (CUSUM) chart has been proposed that uses the exponentially weighted moving average (EWMA) statistic, called the EWMA-CUSUM chart. Similarly, in order to further enhance the sensitivity of the EWMA-CUSUM chart, we propose a new CUSUM chart using the generally weighted moving average (GWMA) statistic, called the GWMA-CUSUM chart, for efficiently monitoring the process mean. The GWMA-CUSUM chart encompasses the existing CUSUM and EWMA-CUSUM charts. Extensive Monte Carlo simulations are used to explore the run length profiles of the GWMA-CUSUM chart. Based on comprehensive run length comparisons, it turns out that the GWMA-CUSUM chart performs substantially better than the CUSUM, EWMA, GWMA, and EWMA-CUSUM charts when detecting small shifts in the process mean. An illustrative example is also presented to explain the implementation and working of the EWMA-CUSUM and GWMA-CUSUM charts.
机译:内存类型的控制图广泛用于过程和服务行业中,用于监视生产过程。原因是它们对小过程干扰快速反应的敏感性。最近,已经提出了一种新的累计总和(CUSUM)图,该图使用了指数加权移动平均(EWMA)统计信息,称为EWMA-CUSUM图。同样,为了进一步增强EWMA-CUSUM图表的敏感性,我们提出了一种新的CUSUM图表,该图表使用了一般加权移动平均(GWMA)统计信息,称为GWMA-CUSUM图表,以有效地监视过程平均值。 GWMA-CUSUM图表包含现有的CUSUM和EWMA-CUSUM图表。广泛的蒙特卡洛模拟用于探索GWMA-CUSUM图表的游程长度分布。根据全面的行程比较,结果发现,当检测到过程平均值的微小变化时,GWMA-CUSUM图表的性能明显优于CUSUM,EWMA,GWMA和EWMA-CUSUM图表。还提供了一个说明性示例,以解释EWMA-CUSUM和GWMA-CUSUM图表的实现和工作。

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