Optimizing Process Monitoring Efficiency through Control Limit Adjustment in Multivariate EWMA Charts

Statistical process control charts have proven to be helpful in process monitoring. The majority of previous research on SPC charts has been on univariate scenarios. This study builds a multivariate exponentially moving average (MEWMA) chart to perform process monitoring in a continuous stirred tank reactor (CSTR). The normal and faulty data were obtained from the Simulink model of CSTR. Smoothing parameter of EWMA was optimized to maximize process monitoring efficiency. False alarm rate (FAR) and fault detection rate (FDR) were used for calculating the monitoring efficiency. A novel control limit calculation combining T² and square prediction error (SPE) is proposed to increase the accuracy of MEWMA technique.