A nonparametric EWMA control chart for monitoring mixed continuous and count data

Abstract

Conventional statistical process control tools monitor either continuous or count data but rarely both simultaneously. While process data are becoming increasingly complex, there will be more data points containing both continuous and count information. In the case of mixed continuous and count data with unknown distributions, the traditional parameter control chart cannot be used to monitor them. It is proposed in this paper a novel nonparametric EWMA control chart to monitor mixed continuous and count data. The mixed continuous and count data are first transformed into categorical data, and then a log-linear model is utilized to analyze correlations between variables, followed by the construction of an EWMA statistic that is used to monitor mixed continuous and count data. Next, the proposed control chart is compared with several improved control charts for monitoring mixed continuous and count data. Based on the numerical simulation results, the control chart presented in this paper provides a superior method of detecting alarm signals in the process compared to some improved control charts. Finally, the proposed control chart is demonstrated to be effective and applicable using the semiconductor manufacturing process dataset from the UC Irvine Machine Learning Repository.

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