Robust scale estimators in statistical quality control: Robust control charts

Control Charts are one of the most powerful tools used to detect aberrant behavior in industrial processes. A valid performance measure for a control chart is the average run length (ARL); which is the expected number of runs to get an out of control signal. The usual Shewart S Control Charts? performance in controlling the process standard deviation is based on the fundamental assumption of normality, which is a rarely consistent one in practice.Robust estimators are of vital importance in Statistics in order to estimate population parameters independent of the data distribution. ?Median Absolute Deviation? (MAD), Sn, and Qn are such estimators for population standard deviation.The aim of this study is to observe performance of Shewart S-Chart for heavy tailed symmetric distributions and propose alternative robust control charts that perform better. Such qualified charts are proposed, whose control limits are obtained by using bootstrap methodology. Monte Carlo simulation study is performed to simulate their performances under normal and non-normal distributions.The findings of the study assert an equal-power design to the use of Shewart S Chart. More importantly, although the proposed design?s false alarm probability (PFA) is slightly more under normal distribution, its PFA is much less than that of Shewart S Chart for heavy tailed symmetric distributions. This design employs the simultaneous use of Sn Chart and Qn Chart.Cauchy model is an important model in specific applications of Electrical Engineering and Physics. Shewart S chart does not work in a Cauchy model and another design is proposed for this model. This second design makes simultaneous use of MAD and Qn Charts.