Model‐based control chart for autoregressive and correlated data
Model‐based control chart for autoregressive and correlated data
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DOI：
10.1002/qre.497
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发表时间：
2002-11
影响因子：
2.3
通讯作者：
Elvira N. Loredo;D. Jearkpaporn;C. Borror
中科院分区：
文献类型：
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作者：
Elvira N. Loredo;D. Jearkpaporn;C. Borror
近年来，自相关过程的统计过程控制受到了极大的关注。这部分是由于测量和数据收集方面的改进，使过程能够以更高的频率进行采样，因此，数据自相关。本文提出了一种基于回归调整的自相关过程监控方法。通过蒙特卡罗模拟比较了基于残差的控制图在平均游程长度方面的性能与基于观察的控制图。一般来说，当数据随时间相关时，基于观测值的控制图表现得很差。在模型正确的假设下，基于残差的控制图对于这里考虑的所有情况都是上级的。这表明使用基于残差的控制图来检测均值偏移。这是特别推荐的化学过程中，往往有级联过程与几个输入，但只有几个输出，其中许多变量是高度自相关的。版权所有© 2002年约翰威利父子有限公司。
In recent years, statistical process control for autocorrelated processes has received a great deal of attention. This is due in part to the improvements in measurement and data collection that allow processes to be sampled at higher frequency rates and, hence, data autocorrelation. A method for monitoring autocorrelated processes based on regression adjustment is presented in this paper. The performance of the residual‐based control chart in terms of the average run length is compared to observation‐based control charts via Monte Carlo simulations. In general, the observation‐based control charts perform very poorly when data are correlated over time. Under the assumption that the model is correct, the residual‐based control charts are superior for all cases considered here. This suggests using a residual‐based control chart to detect the mean shift. This is recommended particularly for chemical processes where there are often cascade processes with several inputs but only a few outputs, and where many of the variables are highly autocorrelated. Copyright © 2002 John Wiley & Sons, Ltd.