Control charts are designed under the normality assumption of the quality characteristic of the process. However, the normality assumption rarely holds in practice. In non-normal conditions, parametric charts tend to display more false alarm rates and invalid out-of-control comparisons. The exponentially weighted moving average chart is a frequently used memory-type control chart for monitoring the process target that only performs effectively under the smoothing parameter's small choices. This study proposes a nonparametric mixed exponentially weighted moving average-progressive mean chart based on sign statistic (NPMEP_SN) under simple and ranked set sampling schemes to address this said drawback. Normal and non-normal distributions are included in this study to observe the proposed chart's in-control behavior and out-of-control efficacy. The prominent feature of the proposed schemes is that it works efficiently in detecting small and persistent shifts in the process location corresponding to the given values of the smoothing parameter. The proposed scheme is also tested under the ranked set sampling scheme to enhance the NPMEP_SN chart's performance (hereafter named “NPMEP_RSN”). The performance of the proposed charts is investigated through simulations using run-length profiles. The proposed schemes were seen to outperform other alternatives, specifically under the ranked set sampling scheme. A real data-set related to the diameter of a piston ring is included as a demonstration of the proposal.

中文翻译：

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开发应用于制造业的敏感非参数混合控制图

控制图是在过程质量特性的正态假设下设计的。然而，正态性假设在实践中很少成立。在非正常情况下，参数图表往往会显示更多的误报率和无效的失控比较。指数加权移动平均图是一种常用的记忆型控制图，用于监控仅在平滑参数的小选择下才有效执行的过程目标。本研究提出了一种基于符号统计的非参数混合指数加权移动平均-渐进平均图 (NPMEP _SN) 在简单的排序集采样方案下解决上述缺点。本研究中包括正态和非正态分布，以观察建议图表的控制行为和失控效果。所提出的方案的突出特点是它可以有效地检测与平滑参数的给定值相对应的过程位置的小而持久的变化。所提出的方案也在排序集抽样方案下进行了测试，以提高 NPMEP _SN图的性能（以下称为“NPMEP _RSN”）。建议图表的性能是通过使用行程长度分布的模拟来研究的。建议的方案被认为优于其他替代方案，特别是在排序集抽样方案下。包括与活塞环直径相关的真实数据集作为提案的演示。