An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts

Computers and Industrial Engineering

Volumn 55, Issue 2, 2008, Pages 475-493

  Guh, Ruey Shiang ^a   Shiue, Yeou Ren ^b  

^a NATIONAL FORMOSA UNIVERSITY   (Taiwan)

^b HUAFAN UNIVERSITY   (Taiwan)

Author keywords

Control charts; Decision tree learning; Multivariate processes; Neural networks; Statistical process control

Indexed keywords

COMPUTER NETWORKS; DECISION THEORY; DECISION TREES; FLOWCHARTING; MATHEMATICAL MODELS; SURFACE TREATMENT;

CONTROL CHARTS; DECISION TREE LEARNING; MULTIVARIATE PROCESSES; NEURAL NETWORKS; STATISTICAL PROCESS CONTROL;

DATA ACQUISITION;

EID: 50349096476     PISSN: 03608352     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cie.2008.01.013     Document Type: Article

References (50)

1. Alwan L.C., and Roberts H.V. The problem of misplaced control limits. Journal of Royal Statistical Society 44 3 (1995) 269-278
2. Bunke H., Dickinson P., Irniger C., and Kraetzl M. Recovery of missing information in graph sequences by means of reference pattern matching and decision tree learning. Pattern Recognition 39 4 (2006) 573-586
3. Cheng C.S. A neural network approach for the analysis of control chart patterns. International Journal of Production Research 35 3 (1997) 667-697
4. 2 chart signals. Computers and Industrial Engineering 47 2-3 (2004) 195-205
5. Crosier R.B. Multivariate generalizations of cumulative sum quality control schemes. Technometrics 30 3 (1988) 291-303
6. Freund Y., and Schapire R.E. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14 5 (1999) 771-780
7. Fuchs C., and Benjamini Y. Multivariate profile charts for statistical process control. Technometrics 36 2 (1994) 182-195
8. Guh R.S. Robustness of the neural network based control chart pattern recognition system to non-normality. International Journal of Quality and Reliability Management 19 1 (2002) 97-112
9. Guh R.S. A hybrid learning-based model for on-line detection and analysis of control chart patterns. Computers and Industrial Engineering 49 1 (2005) 35-62
10. Guh, R.S. (2006). On-line identification and Quantification of mean shifts in bivariate processes using a neural network-based approach. Quality and Reliability Engineering International, Published on-line: doi:10.1002/qre.796.
11. Guh R.S., and Shiue Y.R. On-line identification of control chart pattern using self-organizing approaches. International Journal of Production Research 43 6 (2005) 1225-1254
12. Guh R.S., and Tannock J.D.T. Recognition of control chart concurrent patterns using a neural network approach. International Journal of Production Research 37 8 (1999) 1743-1765
13. Guh R.S., and Tannock J.D.T. A neural network approach to characterize pattern parameters in process control charts. Journal of Intelligent Manufacturing 10 5 (1999) 449-462
14. Hawkins D.M. Multivariate quality control based on regression-adjusted variables. Technometrics 33 1 (1991) 61-75
15. Hawkins D.M. Regression adjustment for variables in multivariate quality control. Journal of Quality Technology 25 3 (1993) 170-182
16. Hayter A.J., and Tsui K.L. Identification and quantification in multivariate quality control problems. Journal of Quality Technology 26 3 (1994) 197-208
17. Ho E.S., and Chang S.I. An integrated neural network approach for simultaneous monitoring of process mean and variance shifts - a comparative study. International Journal of Production Research 37 8 (1999) 1881-1901
18. Holmes D.S., and Mergen A.E. Improving the performance of T-square control chart. Quality Engineering 5 4 (1993) 619-625
19. Hotelling H.H. Multivariate quality control. In: Eisenhart C., Hastay M.W., and Wallis W.A. (Eds). Techniques of statistical analysis (1947), McGraw-Hill, New York
20. Hwarng H.B. Proper and effective training of a pattern recognizer for cyclic data. IIE Transactions 27 6 (1995) 746-756
21. Hwarng H.B. A neural network approach to identifying cyclic behaviour on control charts: A comparative study. International Journal of Systems Science 28 1 (1997) 99-112
22. Hwarng H.B. Simultaneous identification of mean shift and correlation change in AR(1) processes. International Journal of Production Research 43 9 (2005) 1761-11783
23. Hwarng H.B., and Chong C.W. Detecting process non-randomness through a fast and cumulative learning ART-based pattern recognizer. International Journal of Production Research 33 7 (1995) 1817-1833
24. Jackson J.E. Multivariate quality control. Communications in Statistics - Theory and Methods 14 110 (1985) 2657-2688
25. Jackson J.E. A user's guide to principle component (1991), Wiley, New York
26. Johnson R.A., and Wichern D.W. Applied multivariate statistical analysis. 2nd ed. (1992), Prentice-Hall, Englewood Cliffs, NJ
27. Kourti T., and MacGregor J.F. Multivariate SPC methods for process and product monitoring. Journal of Quality Technology 28 4 (1996) 409-428
28. Law A.M., and Kelton W.D. Simulation modelling and analysis (1982), McGraw-Hill, New York
29. Lehman R.S. Computer simulation and Modeling: An introduction (1977), Lawrence Erlbaum, London
30. Li X.B. A scalable decision tree system and its application in pattern recognition and intrusion detection. Decision Support Systems 41 1 (2005) 112-130
31. Lim T.S., Loh W.Y., and Shih Y.S. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning 40 3 (2000) 203-228
32. Lowry C.A., and Montgomery D.C. A review of multivariate control charts. IIE Transactions 27 6 (1995) 800-810
33. Lowry C.A., Woodall W.H., Champ C.W., and Rigdon S.E. Multivariate exponentially weighted moving average control chart. Technometrics 34 1 (1992) 46-53
34. Mason R.L., Champ C.W., Tracy N.D., Wierda S.J., and Young J.C. Assessment of multivariate process control techniques. Journal of Quality Technology 29 2 (1997) 140-143
35. 2 for multivariate control chart interpretation. Journal of Quality Technology 27 2 (1995) 99-108
36. Mason R.L., Tracy N.D., and Young J.C. Monitoring a multivariate step process. Journal of Quality Technology 28 1 (1996) 39-50
37. 2 control chart signals. Journal of Quality Technology 29 4 (1997) 396-406
38. Morrison D.F. Multivariate statistical methods. 2nd ed. (1976), McGraw-Hill, New York
39. NeuralWare. (2001). NeuralWorks Professional Plus Reference Guide. Pittsburgh, PA: NeuralWare Inc.
40. Pignatiello J.J., and Runger G.C. Comparisons of multivariate CUSUM charts. Journal of Quality Technology 22 3 (1990) 173-186
41. Quinlan J.R. Induction of decision trees. Machine Learning 1 1 (1986) 81-106
42. Quinlan J.R. Simplifying decision trees. International Journal of Man-Machine Studies 27 2 (1987) 221-234
43. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufman, Los Allos, CA
44. Quinlan J.R. Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research 4 (1996) 77-90
45. RuleQuest Research Pty Ltd. (2006). See5: An informal tutorial. St Ives, Australia: RuleQuest Research. Available from: http://www.rulequest.com/see5-win.html.
46. Ryan T.P. Statistical methods for quality improvement (1989), Wiley, New York
47. Tracy N.D., Young J.C., and Mason R.L. Multivariate control charts for individual observations. Journal of Quality Technology 24 2 (1992) 88-95
48. Western Electric Company. (1958). Statistical quality control handbook. Indianapolis, IN: AT&T Technologies, Inc.
49. Wierda, S. J. (1994). Multivariate Statistical Process Control. Gromingen Theses in Economics, Management and Organization, Wolters-Noordhoff, Groningen, The Netherlands.
50. Yang B.S., Lim D.S., and Tan C.C. VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table. Expert Systems with Applications 28 4 (2005) 735-742