Multivariate statistical process control for autocorrelated processes
Multivariate statistical process control for autocorrelated processes
复制标题
自相关过程的多元统计过程控制
DOI：
10.1080/00207549608904992
复制
发表时间：
1996
期刊：
影响因子：
--
通讯作者：
G. Runger
中科院分区：
文献类型：
--
作者：
G. Runger
多元统计过程控制常用于自相关最为普遍的化工和加工工业。我们提出了一个产生自相关和互相关的现实模型，并提供了表征过程数据的有用方法。我们展示了我们的模型如何与广泛使用的主成分分析方法相关联，区分可分配原因的类型，并基于非自相关的主成分分解提出了有用的控制统计。即使输入数据是自相关的，也可以通过常规分析开发此统计的控制图。此外，为了描述我们的结果，我们表明输入数据的任何非自相关的线性组合都与我们的控制统计量相关。
Multivariate statistical process control is often used in chemical and process industries where autocorrelation is most prevalent. We present a realistic model that generates autocorrelation and crosscorrelation and provides a useful approach to characterizing process data. We show how our model relates to the widely-used method of principal component analysis, distinguish between types of assignable causes, and present a useful control statistic based on a principal component decomposition that is not autocorrelated. The control chart for this statistic can be developed by a routine analysis even when the input data is autocorrelated. Furthermore, to characterize our results, we show that any linear combination of the input data that is not autocorrelated is related to our control statistic.