Browse > Article
http://dx.doi.org/10.7465/jkdi.2015.26.3.639

Multivariate process control procedure using a decision tree learning technique  

Jung, Kwang Young (Department of Applied Statistics, Chung-Ang University)
Lee, Jaeheon (Department of Applied Statistics, Chung-Ang University)
Publication Information
Journal of the Korean Data and Information Science Society / v.26, no.3, 2015 , pp. 639-652 More about this Journal
Abstract
In today's manufacturing environment, the process data can be easily measured and transferred to a computer for analysis in a real-time mode. As a result, it is possible to monitor several correlated quality variables simultaneously. Various multivariate statistical process control (MSPC) procedures have been presented to detect an out-of-control event. Although the classical MSPC procedures give the out-of-control signal, it is difficult to determine which variable has caused the signal. In order to solve this problem, data mining and machine learning techniques can be considered. In this paper, we applied the technique of decision tree learning to the MSPC, and we did simulation for MSPC procedures to monitor the bivariate normal process means. The results of simulation show that the overall performance of the MSPC procedure using decision tree learning technique is similar for several values of correlation coefficient, and the accurate classification rates for out-of-control are different depending on the values of correlation coefficient and the shift magnitude. The introduced procedure has the advantage that it provides the information about assignable causes, which can be required by practitioners.
Keywords
Computer integrated manufacturing; data mining; decision tree learning; multivariate process control;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Chen, L. H. and Wang, T. Y. (2004). Artificial neural networks to classify mean shifts from multivariate $chi^2$ chart signals. Computers & Industrial Engineering, 47, 195-205.   DOI   ScienceOn
2 Cho, G. Y. (2010). Multivariate Shewhart control charts with variable sampling intervals. Journal of the Korean Data & Information Science Society, 21, 999-1008.
3 Cho, G. Y. and Park, J. S. (2013). Parameter estimation in a readjustment procedure in the multivariate integrated process control. Journal of the Korean Data & Information Science Society, 24, 1275-1283.   DOI   ScienceOn
4 Guh, R. S. (2005). A hybrid learning-based model for on-line detection and analysis of control chart patterns. Computers & Industrial Engineering, 49, 35-62.   DOI   ScienceOn
5 Guh, R. S. (2007). On-line identification and quantification of mean shifts in bivariate processes using a neural network-based approach. Quality and Reliability Engineering International, 23, 367-385.   DOI   ScienceOn
6 Guh, R. S. and Shiue, Y. R. (2005). On-line identification of control chart pattern using self-organizing approaches. International Journal of Production Research, 43, 1225-1254.   DOI   ScienceOn
7 Guh, R. S. and Shiue, Y. R. (2008). An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts. Computers & Industrial Engineering, 55, 475-493.   DOI   ScienceOn
8 Guh, R. S. and Tannock, J. D. T. (1999). Recognition of control chart concurrent pattern using a neural network approach. International Journal of Production Research, 37, 1743-1765.   DOI
9 Ho, E. S. and Chang, S. I. (1999). An integrated neural network approach for simultaneous monitoring of process mean and variance shifts - a comparative study. International Journal of Production Research, 37, 1743-1765.   DOI
10 Hwarng, H. B. (2005). Simultaneous identification of mean shift and correlation change in AR(1) processes. International Journal of Production Research, 43, 1761-1783.   DOI   ScienceOn
11 Quinlan, J. R. (1998). C5.0: An informal tutorial, RuleQuest, Australia.