自相关过程的改进混合指数加权移动平均-累积和控制图

IF 0.9 Q3 STATISTICS & PROBABILITY
Dushyant Tyagi, Vipin Yadav
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引用次数: 1

摘要

统计过程控制(SPC)是监测、管理、分析和恢复过程性能的有效方法。在工业中实施SPC会带来最大的好处,如提高产品质量和减少工艺变化。在处理控制图理论时,我们通常以独立过程观察为前提。但在实际应用中,大多数过程的观测值是自相关的,这降低了控制图应用的能力。通过对传统控制图进行修改,可以消除自相关造成的损失。本文介绍了EWMA和CUSUM制图技术的结合,补充了控制范围的修改。通过将平均运行长度(ARL)与现有控制图进行比较来衡量所述方案的性能。此外,所提到的方案被发现相当好地检测过程中特别小的位移。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Modified Mixed Exponentially Weighted Moving Average-Cumulative Sum Control Charts for Autocorrelated Process
Statistical Process Control (SPC) is an efficient methodology for monitoring, managing, analysing and recuperating process performance. Implementation of SPC in industries results in biggest benefits, as enhanced quality products and reduced process variation. While dealing with the theory of control chart we generally move with the assumption of independent process observation. But in practice usually, for most of the processes the observations are autocorrelated which degrades the ability of control chart application. The loss caused by autocorrelation can be obliterated by making modifications in the traditional control charts. The article presented here refers to a combination of EWMA and CUSUM charting techniques supplementing modifications in the control limits. The performance of the referred scheme is measured by comparing average run length (ARL) with existing control charts. Also, the referred scheme is found reasonably well for detecting particularly smaller displacements in the process.
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来源期刊
CiteScore
1.60
自引率
12.50%
发文量
24
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