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 ...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.展开更多
An automotive body is composed of compliant sheet metal parts.Fast and exactly diagnosing variation sources is very important when assembly variations happen.This paper proposes a diagnosis method of multi fixture var...An automotive body is composed of compliant sheet metal parts.Fast and exactly diagnosing variation sources is very important when assembly variations happen.This paper proposes a diagnosis method of multi fixture variations based on the variation model of compliant sheet metal assembly.The assembly variation model is obtained by using the method of influence coefficients(MIC) and considering the manufacturing variations of compliant parts and multi fixture variations.The measurement point variations induced by part manufacturing variations are firstly removed from the measurement data.The variation patterns of multi fixture variations are constructed by column vectors of fixture variation sensitivity matrix.This method is proved to be feasible for exactly diagnosing the fixture variations and has higher diagnosis efficiency than designated component analysis(DCA).展开更多
文摘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the National Natural Science Foundation of China (No. 50705056)the National High Technology Research and Development Program (863) of China (No.2006AA04Z148)
文摘An automotive body is composed of compliant sheet metal parts.Fast and exactly diagnosing variation sources is very important when assembly variations happen.This paper proposes a diagnosis method of multi fixture variations based on the variation model of compliant sheet metal assembly.The assembly variation model is obtained by using the method of influence coefficients(MIC) and considering the manufacturing variations of compliant parts and multi fixture variations.The measurement point variations induced by part manufacturing variations are firstly removed from the measurement data.The variation patterns of multi fixture variations are constructed by column vectors of fixture variation sensitivity matrix.This method is proved to be feasible for exactly diagnosing the fixture variations and has higher diagnosis efficiency than designated component analysis(DCA).
