Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes

Though traditional control charts have been widely used as effective tools in statistical process control (SPC), they are not applicable in many industrial applications where the process variables are highly auto-correlated. In this study, one new minimal Euclidean distance (MED) based monitoring approach is proposed for enhancing the monitoring mean shifts of auto-correlated processes. Support vector regression (SVR) is used to predict the values of a variable in time series. Through calculating minimal Euclidean distance (MED) values over time series, a novel MED chart is developed for monitoring mean shifts, and it can provide a comprehensive and quantitative assessment for the current process state. The performance of the proposed MED control chart is evaluated based on average run length (ARL). Simulation experiments are conducted and one industrial case is illustrated to validate the effectiveness of the developed MED control chart. The analysis results indicate that the developed MED control chart is more effective than other control charts for small process mean shifts in auto-correlated processes, and it can be used as a promising tool for SPC.