摘要
Since Lowry et al. [1992] proposed a multivariate version of theexponentially weighted moving average (EWMA) control chart, the multivariate EWMA control chart hasbecome more and more popular in monitoring production processes, especially in chemical processes.A major advantage of multivariate EWMA statistics is that it is sensitive to small and moderateshifts in the mean vector. However, when a multivariate EWMA chart issues a signal, it is difficultto identify which variable or set of variables is out of control. In this paper, we introduce anew approach to diagnosing signals from a multivariate EWMA control chart. The implementationprocedure is that when the multivariate EWMA control chart issues a signal, we adopt a univariatediagnostic procedure to identify the variables or/and the principal components that caused thesignal.
Since Lowry et al. [1992] proposed a multivariate version of theexponentially weighted moving average (EWMA) control chart, the multivariate EWMA control chart hasbecome more and more popular in monitoring production processes, especially in chemical processes.A major advantage of multivariate EWMA statistics is that it is sensitive to small and moderateshifts in the mean vector. However, when a multivariate EWMA chart issues a signal, it is difficultto identify which variable or set of variables is out of control. In this paper, we introduce anew approach to diagnosing signals from a multivariate EWMA control chart. The implementationprocedure is that when the multivariate EWMA control chart issues a signal, we adopt a univariatediagnostic procedure to identify the variables or/and the principal components that caused thesignal.
基金
theNationalNaturalScienceFoundationofP .R .ChinaUnderGrantNo .7990 0 0 18andNo .70 372 0 10
andbyAeronauticalScienceFoundationofP .R.ChinaUnderGrantNo .0 2J550 0 1
