Detection and classification mean-shifts in multi-attribute processes by artificial neural networks

International Journal of Production Research

Volumn 46, Issue 11, 2008, Pages 2945-2963

  Niaki, S T A ^a   Abbasi, B ^a  

^a SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Artificial neural network; Multi attribute control chart; Multivariate processes; Process monitoring

Indexed keywords

NEURAL NETWORKS; PROBLEM SOLVING; PROCESS MONITORING;

MULTIVARIATE PROCESSES; PRODUCT NONCONFORMITIES;

PRODUCTION ENGINEERING;

EID: 41849120766     PISSN: 00207543     EISSN: 1366588X     Source Type: Journal    
DOI: 10.1080/00207540601039809     Document Type: Article

References (44)

1. Bishop, C., Neural networks for pattern recognition, 1995 (Oxford University Press: Oxford, UK).
2. Chang, S.I. and Aw, C.A., A neural fuzzy control chart for detecting and classifying process shifts. Int. J. Prod. Res., 1996, 34, 2265-2278.
3. Chang, S.I. and Ho, E.S., A two-stage network approach for process variance change detection and classification. Int. J. Prod. Res., 1999, 37, 1581-1599.
4. Cario, M.C. and Nelson, B.L., Modeling and generating random vectors with arbitrary marginal distributions and correlation matrix. Technical Report, Department of Industrial Engineering and Management Sciences, Northwestern University, USA, 1997.
5. Cheng, C.S., A multi-layer neural network model for detecting changes in the process mean. Compu. Indust. Eng., 1995, 28, 51-61.
6. Cheng, C.S., A neural network approach for analysis of control chart patterns. Int. J. Prod. Res., 1997, 35, 667-697.
7. Fausett, L., Fundamentals of neural networks, 1994 (Prentice Hall: New York, NY).
8. Gadre, M.P. and Rattihalli, R.N., Some group inspection based multi-attribute control charts to identify process deterioration. Econ. Qual. Cont., 2005, 2, 151-164.
9. Gibra, I.N., Economically optimal determination of the parameters of np-control charts. J. Qual. Tech., 1978, 10, 12-19.
10. Golnabi, S. and Houshmand, A.A., Multivariate shewhart x-bar chart. Inter Stat, 1999, 4. A web-based journal (http://interstat.stat. vt.edu/interstat/index/Sep99.html).
11. Guh, R.S. and Hsieh, Y.C., A neural network based model for abnormal pattern recognition of control charts. Compu. & Indust. Eng., 1999, 36, 97-108.
12. Guo, Y. and Dooley, K.J., Identification of change structure in statistical process control. Int. J. Prod. Res., 1992, 30, 1655-1669.
13. Hawkins, D.M., Regression adjustment for variables in multivariate quality control. J. Qual. Tech., 1991, 25, 175-182.
14. Haykin, S., Neural Networks: A Comprehensive Foundation, 1994 (Macmillan Publishing: New York, NY).
15. Hayter, A.J. and Tsui, K.L., Identification and qualification in multivariate quality control problems. J. Qual. Tech., 1994, 26, 197-208.
16. Healy, J.D., A note on multivariate CUSUM procedures. Technometrics, 1987, 29, 409-112.
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. Int. J. Prod. Res., 1999, 37, 1881-1901.
18. Hotelling, H., Multivariate quality control. In Techniques of Statistical Analysis, edited by C. Eisenhart, M. Hastay and W.A. Wallis, 1947 (McGraw-Hill: New York, NY).
19. Hwamg, H.B. and Hubele, N.F., X-bar control chart pattern identification through efficient off-line neural network training. IIE Trans., 1993, 25, 27-40.
20. Jolayemi, J.K., An optimal design of multi-attribute control charts for processes subject to a multiplicity of assignable causes. Appl. Math. & Compu., 2000, 114, 187-203.
21. Jolayemi, J.K., Convolution of independent binomial variables: an approximation method and a comparative study. Compu. Stat. & Data Anal., 1994, 18, 403-417.
22. Kaastra, I. and Boyd, M., Designing a neural network for forecasting financial and economic time series. Neurocomputing, 1996, 10, 215-236.
23. Kourti, T. and MacGregor, J.F., Multivariate SPC methods for process and product monitoring. J. Qual Tech., 1996, 28, 409-428.
24. Larpkiattaworn, S., A neural network approach for multi-attribute process control with comparison of two current techniques and guidelines for practical use. PhD thesis, University of Pittsburgh, USA, 2003.
25. Lowry, C.A. and Montgomery, D.C., A review of multivariate control charts. IIE Trans., 1995, 27, 800-810.
26. Lowry, C.A., Woodall, W.H., Champ, C.W. and Erigdon, S., A multivariate exponentially weighted moving average control chart. Technometrics, 1992, 34, 46-53.
27. Lu, X.S., Xie, M., Goh, T.N. and Lai, C.D., Control chart for multivariate attribute processes. Int. J. Prod. Res., 1998, 36, 3477-3489.
28. Lucas, J.M. and Saccucci, M.S., Exponentially weighted moving average control schemes: properties and enhancements. Technometrics, 1990, 32, 1-10.
29. Lucas, J.M. and Crosier, R.B., Fast initial response for CUSUM quality control schemes: give your CUSUM a head start. Technometrics, 1982, 24, 199-2054.
30. Marcucci, M., Monitoring multinomial processes. J. Qual. Tech., 1985, 17, 86-91.
31. Martin, E.B. and Morris, A.J., Multivariate statistics and neural networks in process fault detection. IEE Colloquium (Digest), 1995, 079, 7/1-7/8.
32. Montgomery, D.C., Introduction to Statistical Quality Control, 5th ed., 2003 (John Wiley and Sons: New York, NY).
33. Niaki, S.T.A. and Abbasi, B., Fault diagnosis in multivariate control charts using artificial neural networks. J. Qual. & Reliab. Eng. Int., 2005, 21, 825-840.
34. Niaki, S.T.A. and Abbasi, B., NORTA and neural networks based method to generate random vectors with arbitrary marginal distributions and correlation matrix, in Proceedings of the 17th IASTED International Conference on Modeling and Simulation, Montreal, Canada, 2006a.
35. Niaki, S.T.A. and Abbasi, B., A symmetric rectangular region for multi-attribute C. control chart, in Proceedings of the 36th International Conference on Computer and Industrial Engineering, Taipei, Taiwan, 2006b.
36. Patel, H.J., Quality control methods for multivariate binomial and Poisson distributions. Technometrics, 1973, 15, 103-112.
37. Patterson, D., Artificial Neural Networks, 1996 (Prentice Hall: Singapore).
38. Pignatiello, J.J. and Runger, G.C., Comparisons of multivariate CUSUM charts. J. Qual. Tech., 1990, 22, 173-186.
39. Pugh, G.A., Synthetic neural networks for process control. Compu. Indust. Eng., 1989, 17, 24-26.
40. Pugh, G.A., A comparison of neural networks to SPC charts. Int. J. Prod. Res., 1991, 21, 253-255.
41. Runger, G.C., Projections and the U2 multivariate control chart. J. Qual. Tech., 1996, 2, 313-319.
42. Wang, T.-Y. and Chen, L.-H., Mean shift detection in multivariate process: a neural fuzzy approach. J. Intel. Manuf., 2002, 13, 211-221.
43. Wilson, D.J.H., Irwin, G.W. and Lightbody, G., Neural networks and multivariate. IEE Colloquium (Digest), 1997, 174, 5/1-5/5.
44. Woodall, W.H. and Ncube, M.M., Multivariate CUSUM, quality-control procedures. Technometrics, 1985, 27, 285-292.