一种用于监测过程变异性的双加权移动平均控制图。
A double generally weighted moving average control chart for monitoring the process variability.
作者信息
Alevizakos Vasileios, Chatterjee Kashinath, Koukouvinos Christos, Lappa Angeliki
机构信息
Department of Mathematics, National Technical University of Athens, Zografou, Greece.
Department of Population Health Sciences, Division of Biostatistics and Data Science, Augusta University, Augusta, GA, USA.
出版信息
J Appl Stat. 2022 Apr 22;50(10):2079-2107. doi: 10.1080/02664763.2022.2064977. eCollection 2023.
In the present article, a double generally weighted moving average (DGWMA) control chart based on a three-parameter logarithmic transformation is proposed for monitoring the process variability, namely the -DGWMA chart. Monte-Carlo simulations are utilized in order to evaluate the run-length performance of the -DGWMA chart. In addition, a detailed comparative study is conducted to compare the performance of the -DGWMA chart with several well-known memory-type control charts in the literature. The comparisons indicate that the proposed one is more efficient in detecting small shifts, while it is more sensitive in identifying upward shifts in the process variability. A real data example is given to present the implementation of the new -DGWMA chart.
在本文中，提出了一种基于三参数对数变换的双广义加权移动平均（DGWMA）控制图，用于监测过程变异性，即-DGWMA控制图。利用蒙特卡罗模拟来评估-DGWMA控制图的运行长度性能。此外，还进行了详细的比较研究，以将-DGWMA控制图的性能与文献中几种著名的记忆型控制图进行比较。比较结果表明，所提出的控制图在检测小偏移时更有效，同时在识别过程变异性的向上偏移时更敏感。给出了一个实际数据示例，以展示新的-DGWMA控制图的实施情况。
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