The quadruple exponentially weighted moving average control chart

IF 2.3 2区工程技术 Q3 ENGINEERING, INDUSTRIAL Quality Technology and Quantitative Management Pub Date : 2021-11-12 DOI:10.1080/16843703.2021.1989141

Vasileios Alevizakos, K. Chatterjee, C. Koukouvinos

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引用次数: 2

Abstract

ABSTRACT The exponentially weighted moving average (EWMA) control chart is a very popular memory-type chart and also effective in detecting small shifts in the process mean. Several modifications of the EWMA chart, such as the double and triple EWMA charts (regarded as DEWMA and TEWMA charts, respectively) have been developed to enhance its performance in detecting small shifts. In the present article, we propose the quadruple EWMA chart (regarded as QEWMA chart) in order to improve much more the detection ability of the EWMA chart. The run-length characteristics of the proposed chart are evaluated by performing Monte Carlo simulations. Comparing with the EWMA, DEWMA and TEWMA charts, it is found that the QEWMA chart outperforms its competitors for small shifts. Moreover, it is shown that the proposed chart is more in-control (IC) robust under several non-normal distributions than the other charts, especially for a medium value of the smoothing parameter. The effect of inertia on the performance of the QEWMA chart is also investigated as a part of this article. Finally, two examples are provided to demonstrate the application of the proposed chart.

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四重指数加权移动平均线控制图

指数加权移动平均（EWMA）控制图是一种非常流行的记忆型图表，在检测过程平均值的小偏移方面也很有效。对EWMA图进行了一些修改，例如双EWMA图和三EWMA图（分别被视为DEWMA图和TEWMA图），以增强其检测小偏移的性能。在本文中，我们提出了四重EWMA图（称为QEWMA图），以进一步提高EWMA图的检测能力。通过进行蒙特卡罗模拟来评估所提出图表的行程长度特性。与EWMA、DEWMA和TEWMA图表相比，发现QEWMA图表在小的变化方面优于其竞争对手。此外，与其他图表相比，所提出的图表在几个非正态分布下更具控制（IC）鲁棒性，尤其是在平滑参数为中等值的情况下。惯性对QEWMA图表性能的影响也是本文的一部分。最后，通过两个实例说明了该图的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Quality Technology and Quantitative Management ENGINEERING, INDUSTRIAL-OPERATIONS RESEARCH & MANAGEMENT SCIENCE

CiteScore

5.10

自引率

21.40%

发文量

审稿时长

>12 weeks

期刊介绍： Quality Technology and Quantitative Management is an international refereed journal publishing original work in quality, reliability, queuing service systems, applied statistics (including methodology, data analysis, simulation), and their applications in business and industrial management. The journal publishes both theoretical and applied research articles using statistical methods or presenting new results, which solve or have the potential to solve real-world management problems.