Investigation and characterization of a control scheme for multivariate quality control

Abstract

Abstract A new scheme for multivariate statistical quality control is investigated and characterized. The control scheme consists of three steps and it will identify any out‐of‐control samples, select the subset of variables that are out of control, and diagnose the out‐of‐control variables. A new control variable selection algorithm, the backward selection algorithm, and a new control variable diagnosis method, the hyperplane methods, are proposed. It is shown by simulation that the control scheme is useful in cases where the process variables are correlated and where they are uncorrelated.

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