Process Monitoring Using Robust Regression Control Charts

ABSTRACT Control charts are statistical process control tools that aim to analyse and monitor a certain quality characteristic. In some situations, the quality characteristic to be monitored is correlated with one or more control variables, which suggests the use of a regression control chart (RCC). However, outliers are commonly seen in practice, and they can affect the coefficient estimates and control limits of traditional RCCs. For this reason, robust RCCs are strongly recommended, such that outlier observations do not affect model parameter estimates and control limits. This paper considers new robust regression Shewhart‐type control charts based on the exponential‐type kernel and mean absolute deviation estimators. To evaluate the performance of the proposed control charts, we conduct an extensive Monte Carlo simulation study where we discuss the ability of the control charts to detect changes in the process, both in in‐control and out‐of‐control settings, taking into account different percentages of outliers and sample sizes. The numerical results indicate that the proposed robust RCCs performed well compared to their competitors. Finally, to demonstrate the applicability of the control charts proposed in this paper, we present and discuss an empirical application to temperature monitoring data in Sydney, Australia.