Table of Contents
International Journal of Quality, Statistics, and Reliability
Volume 2010, Article ID 180293, 9 pages
http://dx.doi.org/10.1155/2010/180293
Research Article

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

Faculty of Industrial Technology, Valaya Alongkorn Rajabhat University (VRU). 1 Moo 20 Phaholyothin Road, Klongluang, Pathum Thani 13180, Thailand

Received 13 December 2009; Accepted 8 July 2010

Academic Editor: Shuen-lin Jeng

Copyright © 2010 Karin Kandananond. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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. 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.