Effectively Monitoring the Performance of Integrated Process Control Systems under Nonstationary Disturbances

Abstract

The objective of this paper is to quantify the effect of autocorrelation coefficients, shift magnitude, types of control charts, types of controllers, and types of monitored signals on a control system. Statistical process control (SPC) and automatic process control (APC) were studied under non-stationary stochastic disturbances characterized by the integrated moving average model, ARIMA (0,1,1). A process model was simulated to achieve two responses, mean squared error (MSE) and average run length (ARL). A factorial design experiment was conducted to analyze the simulated results. The results revealed that not only shift magnitude and the level of autocorrelation coefficients, but also the interaction between these two factors, affected the integrated system performance. It was also found that the most appropriate combination of SPC and APC is the utilization of the minimum mean squared error (MMSE) controller with the Shewhart moving range (MR) chart, while monitoring the control signal (X) from the controller. Therefore, integrating SPC and APC can improve process manufacturing, but the performance of the integrated system is significantly affected by process autocorrelation. Therefore, if the performance of the integrated system under non-stationary disturbances is correctly characterized, practitioners will have guidelines for achieving the highest possible performance potential when integrating SPC and APC.