Monitoring Industrial Processes with Robust Control Charts

Abstract

The Shewhart control charts, used for monitoring industrial processes, are the most popular tools in Statistical Process Control (SPC). They are usually developed under the assumption of independent and normally distributed data, an assumption rarely true in practice, and implemented with estimated control limits. But in general, we essentially want to control the process mean value and the process standard deviation, independently of the data distribution. In order to monitor these parameters, it thus seems sensible to advance with control charts based on robust statistics, because these statistics are expected to be more resistant to moderate changes in the underlying process distribution. In this paper, we investigate the advantage of using control charts based on robust statistics. Apart from the traditional control charts, the sample mean and the sample range charts, we consider robust control charts based on the total median and on the total range statistics, for monitoring the process mean value and the process standard deviation, respectively. Through the use of Monte Carlo simulations, we compare these charts in terms of robustness and performance.