Browse > Article

Comparison of monitoring the output variable and the input variable in the integrated process control  

Lee, Jae-Heon (Department of Applied Statistics, Chung-Ang University)
Publication Information
Journal of the Korean Data and Information Science Society / v.22, no.4, 2011 , pp. 679-690 More about this Journal
Abstract
Two widely used approaches for improving the quality of the output of a process are statistical process control (SPC) and automatic process control (APC). In recent hybrid processes that combine aspects of the process and parts industries, process variations due to both the inherent wandering and special causes occur commonly, and thus simultaneous application of APC and SPC schemes is needed to effectively keep such processes close to target. The simultaneous implementation of APC and SPC schemes is called integrated process control (IPC). In the IPC procedure, the output variables are monitored during the process where adjustments are repeatedly done by its controller. For monitoring the APC-controlled process, control charts can be generally applied to the output variable. However, as an alternative, some authors suggested that monitoring the input variable may improve the chance of detection. In this paper, we evaluate the performance of several monitoring statistics, such as the output variable, the input variable, and the difference variable, for efficiently monitoring the APC-controlled process when we assume IMA(1,1) noise model with a minimum mean squared error adjustment policy.
Keywords
Automatic process control; control chart; input variable; integrated process control; output variable; statistical process control;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Reynolds, M., Jr. and Park, C. (2010). CUSUM charts for detecting special causes in integrated process control. Quality and Reliability Engineering International, 26, 199-221.   DOI   ScienceOn
2 Runger, G., Testik, M. C. and Tsung, F. (2006). Relationships among ontrol charts used with feedback control. Quality and Reliability Engineering International, 22, 877-887.   DOI   ScienceOn
3 Tsung, F. and Tsui, K.-L. (2003). A mean-shift pattern study on integration of SPC and APC for process monitoring. IIE Transactions, 35, 231-242.   DOI   ScienceOn
4 Vander Wiel, S. A. (1996). Monitoring processes that wander using integrated moving average models. Technometrics, 38, 139-151.
5 Pan, R. and Del Castillo, E. (2003). Integration of sequential process adjustment and process monitoring techniques. Quality and Reliability Engineering International, 19, 371-386.   DOI   ScienceOn
6 Park, C. (2007). An algorithm for the properties of the integrated process control with bounded adjustments and EWMA monitoring. International Journal of Production Research, 45, 5571-5587.   DOI   ScienceOn
7 Box, G. E. P. and Kramer, T. (1992). Statistical process control and feedback adjustment - A discussion. Technometrics, 34, 251-285.   DOI   ScienceOn
8 Park, C. and Lee, J. (2008). An integrated process control scheme based on the future loss. The Korean Journal of Applied Statistics, 21, 247-264.   DOI
9 Park, C. and Lee, J. (2009). A readjustment procedure after signalling in the integrated process control. Communications of the Korean Statistics Society, 16, 429-436.   DOI
10 Park, C. and Reynolds, M., Jr. (2008). Economic design of an integrated process control procedure with repeated adjustments and EWMA monitoring. Journal of the Korean Statistical Society, 37, 155-174.   DOI   ScienceOn
11 Hu, S. J. and Roan, C. (1996). Change patterns of time series-based control charts. Journal of Quality Technology, 28, 302-312.
12 Nembhard, H. B. and Chen, S. (2007). Cuscore control charts for generalized feedback-control systems. Quality and Reliability Engineering International, 23, 483-502.   DOI   ScienceOn
13 Jiang, W. (2004). A joint monitoring scheme for automatically controlled processes. IIE Transactions, 36, 1201-1210.   DOI   ScienceOn
14 Jiang, W. and Tsui, K.-L. (2002). SPC monitoring of MMSE- and PI-controlled processes. Journal of Quality Technology, 34, 384-398.
15 Lee, J. and Kim, M. (2010). Procedure for monitoring special causes and readjustment in ARMA(1,1) noise model. Journal of the Korean Data & Information Science Society, 21, 841-852.
16 Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1994). Time series analysis, forecasting and control, 3rd Ed., Prentice Hall, Englewood Cliffs, New Jersey.