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Enhanced generally weighted moving average variance charts for monitoring process variance with individual observations

机译：增强的一般加权移动平均方差图，用于通过单个观察值监视过程方差

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摘要

The generally weighted moving average variance (GWMAV) chart is effective in detecting increases in process variance when only individual observations are available. Recently, the combination of exponentially weighted moving average and cumulative sum (CUSUM) charts for the effective detection of small process shifts has emerged. Inspired by the features, we propose the mixed GWMAV-CUSUM chart and its reverse order CUSUM-GWMAV to enhance the detection ability of the GWMAV chart and compare with the existing counterparts. Numerical simulation revealed that the mixed GWMAV-CUSUM and mixed CUSUM-GWMAV charts are sensitive to small upward shifts in the process variance and efficient structures compared with their prototypes and their separate charts, that is, GWMAV and CUSUM charts. An industrial dataset was used to illustrate the application of the proposed mixed charts.
机译：当只有个别观察可用时，一般加权的移动平均方差（GWMAV）图可有效地检测过程方差的增加。最近，已经出现了指数加权移动平均值和累积和（CUSUM）图的组合，用于有效地检测小过程偏移。受这些功能的启发，我们提出了混合GWMAV-CUSUM图表及其反序CUSUM-GWMAV，以增强GWMAV图表的检测能力并与现有同类图表进行比较。数值模拟显示，与GWMAV和CUSUM图相比，混合GWMAV-CUSUM和CUSUM-GWMAV混合图对过程方差和有效结构的小幅上移敏感，而对它们的原型和单独的图而言。工业数据集用于说明所提出的混合图表的应用。

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