Rank-based EWMA procedure for sequentially detecting changes of process location and variability

Abstract

This paper presents a study of a new procedure, which is based on integrating a powerful nonparametric test for the two-sample problem and EWMA control scheme to online sequential monitoring. The proposed procedure, based on individual observation per sample, can be used to monitor the location and the scale parameters of a univariate continuous distribution, simultaneously. An iterative computation procedure is developed for computing the monitoring statistics. A search algorithm for the control limit based on Monte-Carlo simulation and bisection method is derived and a table is provided. The sensitivity analysis on the procedure is studied in detail. Monte-Carlo simulation results show that the proposed procedure is quite robust to nonnormally distributed data, and moreover, it is efficient in detecting various process shifts. A real data example from a chemical reaction process is shown to illustrate the application of our proposed procedure.

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