Control chart is a useful tool to monitor the performance of the industrial or production processes. Control charts are mostly adopted to detect unfavorable variations in process location (mean) and dispersion (standard deviation) parameters. In the literature, many control charts are designed for the monitoring of process variability under the assumption that the process mean is constant over time and the standard deviation is independent of the mean. However, for many real‐life processes, the standard deviation may be proportional to mean, and hence it is more appropriate to monitor the process coefficient of variation (CV). In this study, we are proposing a design structure of the Shewhart type CV control chart under neoteric ranked set sampling with an aim to improve the detection ability of the usual CV chart. A comprehensive simulation study is conducted to evaluate the performance of the proposed $C{V}_{[\mathrm{NRSS}]}$ chart in terms of $ARL$, $MDRL,$ and $SDRL$ measures. Moreover, the comparison of $C{V}_{[\mathrm{NRSS}]}$ chart is made with the existing competitive charts, based on simple random sampling, ranked set sampling (RSS), median RSS, and extreme RSS schemes. The results revealed that the proposed chart has better detection ability as compared to all existing competitive charts. Finally, a real‐life example is presented to illustrate the working of the newly proposed CV chart.

中文翻译：

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有效监测变异系数并应用于化学反应器过程

控制图是监视工业或生产过程性能的有用工具。控制图通常用于检测工艺位置（平均值）和分散（标准偏差）参数的不利变化。在文献中，在过程均值随时间恒定且标准偏差与均值无关的假设下，设计了许多控制图用于监视过程变异性。但是，对于许多实际过程，标准偏差可能与平均值成正比，因此更适合监视过程变异系数（CV）。在这项研究中，我们提出了在现代等级集抽样下的Shewhart型CV控制图的设计结构，目的是提高常规CV图的检测能力。$C{\mathrm{伏特}}_{[\mathrm{NRSS}]}$ 图表 $\mathrm{一种}\mathrm{[R}\mathrm{大号}$， $\mathrm{中号}d\mathrm{[R}\mathrm{大号}，$ 和 $\mathrm{小号}d\mathrm{[R}\mathrm{大号}$措施。而且，比较$C{\mathrm{伏特}}_{[\mathrm{NRSS}]}$基于简单的随机抽样，排名集抽样（RSS），中位数RSS和极限RSS方案，该图表是使用现有竞争图表制作的。结果表明，与所有现有竞争图表相比，拟议图表具有更好的检测能力。最后，给出了一个真实的例子来说明新提出的CV图表的工作。