Identifying source(s) of out-of-control signals in multivariate manufacturing processes using selective neural network ensemble

Engineering Applications of Artificial Intelligence

Volumn 22, Issue 1, 2009, Pages 141-152

  Yu, Jianbo ^a   Xi, Lifeng ^a   Zhou, Xiaojun ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Monte Carlo simulation; Multivariate control chart; Multivariate manufacturing process; Pattern recognition; Selective neural network ensemble; Statistical process control

Indexed keywords

CUSTOMER SATISFACTION; FLOWCHARTING; INDUSTRIAL APPLICATIONS; INDUSTRIAL ENGINEERING; MONTE CARLO METHODS; MULTIVARIANT ANALYSIS; NEURAL NETWORKS; PATTERN RECOGNITION; PRODUCTION ENGINEERING; QUALITY ASSURANCE; QUALITY CONTROL; QUALITY FUNCTION DEPLOYMENT; STATISTICAL PROCESS CONTROL; SURFACE TREATMENT; TOTAL QUALITY MANAGEMENT; VEGETATION;

ASSIGNABLE CAUSES; BIVARIATE; CONTROL SIGNALS; CORRECTIVE ACTIONS; ENSEMBLE APPROACHES; GENERALIZATION PERFORMANCES; HEURISTIC APPROACHES; MONTE CARLO SIMULATION; MULTIVARIATE CONTROL CHART; MULTIVARIATE MANUFACTURING PROCESS; MULTIVARIATE PROCESSES; MULTIVARIATE QUALITY CONTROLS; MULTIVARIATE STATISTICAL PROCESS CONTROLS; NOISE TOLERANCES; PATTERN IDENTIFICATIONS; POTENTIAL APPLICATIONS; RAPID ANALYSES; REAL TIMES; SELECTIVE NEURAL NETWORK ENSEMBLE;

PROCESS CONTROL;

EID: 58249092619     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2008.05.009     Document Type: Article

References (43)

1. Alt F.B. Multivariate quality control. In: kotz S., Johnson N.L., and Read C.R. (Eds). The Encyclopaedia of Statistical Science (1984), Wiley, New York 110-122
2. 2 control chart signals. IIE Transactions 38 (2006) 647-657
3. Battiti R. Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks 5 (1994) 537-550
4. Bersimis S., Psarakis S., and Panaretos J. Multivariate statistical process control charts: an overview. Quality and Reliability Engineering International 23 5 (2006) 517-543
5. Breiman L. Bagging predictors. Machine Learning 24 (1996) 123-140
6. 2 chart signals. Computers and Industrial Engineering 47 (2004) 195-205
7. Cheng C.S. A neural network approach for the analysis of control chart patterns. International Journal of Production Research 35 (1997) 667-697
8. Crosier R.B. Multivariate generalizations of cumulative sum quality control schemes. Technometrics 30 (1988) 291-303
9. Efron B., and Tibshirani R. An Introduction to the Bootstrap (1992), Chapman & Hall, New York
10. Geman S., Bienenstock E., and Doursat R. Neural networks and the bias/variance dilemma. Neural Computation 4 (1992) 1-58
11. Guh R.S. Real-time pattern recognition in statistical process control: a hybrid neural network/decision tree-based approach. Proceedings of the Institution of Mechanical Engineers, Part B. Journal of Engineering Manufacture (IMechE 2005) 219 3 (2005) 283-298
12. Guh R.S. A hybrid learning-based model for on-line detection and analysis of control chart patterns. Computers and Industrial Engineering 49 (2005) 35-62
13. Guh R.S. On-line identification and quantification of mean shifts in bivariate processes using a neural network-based approach. Quality and Reliability Engineering International 23 (2007) 367-385
14. Gutta S., and Wechsler H. Face recognition using hybrid classifier systems. Proceedings of the ICNN-96, Washington, DC (1996), IEEE Computer Society Press, Los Alamitos, CA 1017-1022
15. Hansen L.K., and Salamon P. Neural network ensembles,. IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1990) 993-1001
16. Hassan, A., 2002. On-line recognition of developing control chart patterns. Ph.D. Thesis, Universiti Teknologi, Malaysia.
17. Hassan A., Shariff Nabi Baksh M., Shaharoun A.M., and Jamaluddin H. Improved SPC chart pattern recognition using statistical features. International Journal Production Research 41 7 (2003) 1587-1603
18. Hotelling H. Multivariate quality control-illustrated by the air testing of sample bombsights. In: Eisenhart C., Hastay M.W., and Wallis W.A. (Eds). Techniques of Statistical Analysis (1947), McGraw-Hill, New York 11-184
19. Hwarng H.B., and Chong C.W. Detecting process non-randoms through a fast and cumulative learning ART-based pattern recognizer. International Journal of Production Research 33 (1995) 1817-1833
20. Jackson J.E. Multivariate quality control. Communications in statistics-theory and methods 14 (1985) 2657-2688
21. Kennedy J., and Eberhart R. A discrete binary version of the particle swarm optimization. Proceedings of the IEEE International Conference on Computational Cybernetics and Simulation (1997), IEEE, Piscataway, NJ 4104-4108
22. Krogh A., and Vedelsby J. Neural network ensembles, cross-section, and active learning. Advances in Neural Information Proc1 Systems vol. 71 (1995), MIT Press, MA 231-238
23. Low C.Y., Hsu C.M., and Yu F.J. Analysis of variations in a multi-variate process using neural networks. International Journal of Advanced Manufacturing Technology 22 (2003) 911-921
24. Lowry C.A., and Montgomery D.C. A review of multivariate control charts. IIE Transaction 27 (1995) 800-810
25. Lowry C.A., Woodall W.H., Champ C.W., and Rigdon S.E. A multivariate exponentially weighted moving average control chart. Technometrics 34 (1992) 46-53
26. Maqsood I., Khan M.R., and Abraham A. An ensemble of neural networks for weather forecasting. Neural Computing and Application 13 (2004) 112-122
27. 2 for multivariate control chart interpretation. Journal of Quality Technology 27 2 (1995) 99-108
28. 2 control chart signals. Journal of Quality Technology 29 4 (1997) 396-406
29. 2 multivariate quality procedures. The Statistician 36 (1987) 571-583
30. Niaki S.T.A., and Abbasi B. Fault diagnosis in multivariate control charts using artificial neural networks. Quality and Reliability Engineering International 21 (2005) 825-840
31. Opitz D. Feature selection for ensembles. Proceedings of the 16th National Conference on Artificial Intelligence (1999), AAAI Press, Washington, DC, USA 379-384
32. Partridge D. Network generalization differences quantified. Neural Networks 9 2 (1996) 263-271
33. Perrone M.P., and Cooper L.N. When networks disagree: Ensemble method for neural networks. In: Mammone R.J. (Ed). Artificial Neural Networks for Speed and Vision (1993), Chapman & Hall, New York 126-142
34. Pham D.T., and Oztemel E. Control chart pattern recognition using neural networks. Journal of Systems Engineering 2 (1992) 256-262
35. Pham D.T., and Oztemel E. Control chart pattern recognition using learning vector quantization networks. International Journal of Production Research 32 (1994) 721-729
36. Pignatiello J.J., and Runger G.C. Comparisons of multivariate CUSUM charts. Journal of Quality Technology 22 (1990) 173-186
37. Plummer J. Tighter process control with neural networks. AI Expert 8 (1993) 49-55
38. Schapire R.E. The strength of weak learnability. Machine Learning 5 (1990) 197-227
39. Smith A.E. X-bar and R control chart interpretation using neural computing. International Journal of Production Research 32 (1994) 309-320
40. Wang T.Y., and Chen L.H. Mean shifts detection and classification in multivariate process: a neural-fuzzy approach. Journal of Intelligent Manufacturing 13 (2002) 211-221
41. Woodall W.H., and Ncube M.M. Multivariate CUSUM quality-control procedures. Technometrics 27 (1985) 285-292
42. Zhou Z.H., Wu J.X., and Tang W. Ensembling neural networks: many could be better than all. Artificial Intelligence 137 (2002) 239-263
43. Zorriassatine F., Tannock J.D.T., and Brien C.O. Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes. Computers and Industrial Engineering 44 (2003) 385-408