On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines

International Journal of Production Research

Volumn 50, Issue 22, 2012, Pages 6288-6310

  Du, Shichang ^a   Lv, Jun ^b   Xi, Lifeng ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

Ensemble classification; Multivariate statistical process control; Process mean shifts; Support vector machine

Indexed keywords

CLASSIFICATION ACCURACY; CORRECTIVE ACTIONS; ENSEMBLE CLASSIFICATION; MULTI-CLASS; MULTI-CLASS SUPPORT VECTOR MACHINES; MULTIVARIATE CONTROL CHARTS; MULTIVARIATE MANUFACTURING PROCESS; MULTIVARIATE STATISTICAL PROCESS CONTROL; OUT-OF-CONTROL SIGNALS; PARTICLE SWARM OPTIMISATION; PROCESS MEAN SHIFTS; QUALITY ENGINEERS; REAL APPLICATIONS; ROOT CAUSE; SIMULATION EXPERIMENTS;

PARTICLE SWARM OPTIMIZATION (PSO); SIMULATED ANNEALING; STATISTICAL PROCESS CONTROL;

SUPPORT VECTOR MACHINES;

EID: 84868228150     PISSN: 00207543     EISSN: 1366588X     Source Type: Journal    
DOI: 10.1080/00207543.2011.631596     Document Type: Article

References (55)

1. Anderson, T.W., 2003. An introduction to multivariate statistical analysis. 3rd ed. New York: John Wiley.
2. Aparisi, F., Avendano, G., and Sanz, J., 2006. Techniques to interpret T2 control chart signals. IIE Transactions, 38 (3), 647-657. (Pubitemid 43820943)
3. Bersimis, S., Psarakis, S., and Panaretos, J., 2007. Multivariate statistical process control charts: an overview. Quality and Reliability Engineering International, 23 (5), 517-543. (Pubitemid 47245685)
4. Box, G.E.P. and Cox, D.R., 1964. An analysis of transformations. Journal of the Royal Statistical Society (B), 26 (2), 211-252.
5. Breiman, L., 1996. Bagging predictors. Machine Learning, 24 (2), 123-140. (Pubitemid 126724382)
6. Chan, J., Huang, C., and DeFries, R., 2001. Enhanced algorithm performance for land cover classification from remotely sensed data using bagging and boosting. IEEE Transactions on Geoscience and Remote Sensing, 39 (3), 693-695. (Pubitemid 32400437)
7. Chang, S.I. and Aw, C.A., 1996. A neural fuzzy control chart for detecting and classifying process mean shifts. International Journal of Production Research, 34 (8), 2265-2278. (Pubitemid 126525296)
8. Chang, S.I. and Ho, E.S., 1999. A two-stage neural network approach for process variance change detection and classification. International Journal of Production Research, 37 (7), 1581-1599.
9. Chen, S., Cowan, C.F.N., and Grant, P.M., 1991. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transaction on Neural Networks, 2 (2), 302-309.
10. Cheng, C.S., 1995. A multi-layer neural network model for detecting changes in the process mean. Computer and Industrial Engineering, 28 (1), 51-61.
11. Cheng, C.S. and Cheng, H.P., 2008. Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines. Expert Systems with Applications, 35 (1-3), 198-206.
12. Chinnam, R.B., 2002. Support vector machines for recognising shifts in correlated and other manufacturing processes. International Journal of Production Research, 40 (17), 4449-4466.
13. Cook, D.F. and Chiu, C.C., 1998. Using radial basis function neural networks to recognise shifts in correlated manufacturing process parameters. IIE Transactions, 30 (3), 227-234. (Pubitemid 128728199)
14. Cook, D.F., Zobel, C.W., and Nottingham, Q.J., 2001. Utilisation of neural networks for the recognition of variance shifts in correlated manufacturing process parameters. International Journal of Production Research, 39 (17), 3881-3887. (Pubitemid 35165489)
15. Cortes, C. and Vapnik, V., 1995. Support vector network. Machine learning, 20 (3), 273-297.
16. Crammer, K. and Singer, Y., 2000. On the learnability and design of output codes for multiclass problems. Machine Learning, 47 (2-3), 201-233. (Pubitemid 34126110)
17. Cristianini, N. and Shawe-Taylor, J., 2000. A introduction to support vector machines and other kernel-based learning methods. Cambridge, UK: Cambridge University Press.
18. Du, S. and Xi, L., 2010. An integrated system for on-line intelligent monitoring and identifying process variability and its application. International Journal of Computer Integrated Manufacturing, 23 (6), 529-542.
19. Du, S., et al., 2008. Product lifecycle-oriented quality and productivity improvement based on stream of variation methodology. Computers in Industry, 59 (2-3), 180-192.
20. Eberhart, C.R. and Kennedy, J., 1995. Particle swarm optimisation. Proceedings of the IEEE international conference on neural networks, date, Piscataway, NJ. Place: Publisher, 1942-1948.
21. Eberhart, C.R. and Kennedy, J., 1995. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, 27 November-1 December, Perth, Australia. Piscataway, NJ: IEEE, 1942-1948.
22. Eom, J.H., Kim, S.C., and Zhang, B.T., 2008. AptaCDSS-E: A classifier ensemble based clinical decision support system for cardiovascular disease level prediction. Expert System with Application, 34 (4), 2465-2479.
23. Freund, Y. and Schapire, R., 1997. A decision-theoretic generalisation of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55 (1), 119-139. (Pubitemid 127433398)
24. 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 (8), 1881-1901.
25. Hsu, C.W. and Lin, C.J., 2002. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 13 (2), 415-425.
26. Hwarng, H.B., 2004. Detecting process mean shift in the presence of autocorrelation: A neural-network based monitoring scheme. International Journal of Production Research, 42 (3), 573-595.
27. Gordon, J.J., et al., 2006. Improved prediction of bacterial transcription start sites. Bioinformatics, 22 (2), 142-148. (Pubitemid 43205399)
28. 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 (3), 367-385.
29. 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 (2), 475-493.
30. Gunn, S.R., 1998. Support vector machines for classification and regression. Technical Report. University of Southampton.
31. Gupta, A.K., 2010. Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression. International Journal of Product Research, 48 (3), 763-778.
32. Johnson, R. A, and Wichern, D. W., 2007. Applied multivariate statistical analysis, 6th ed., Upper Saddle River, NJ: Prentice Hall.
33. Kourti, T. and MacGregor, J.F., 1996. Multivariate SPC methods for process and product monitoring. Journal of Quality Technology, 28 (4), 409-428. (Pubitemid 126588616)
34. Lei, Z., Yang, Y., and Wu, Z., 2006. Ensemble of support vector machine for text independent speaker recognition. International Journal of Computer Science and Network Security, 6 (5A), 163-167.
35. Li, J., Jin, J., and Shi, J., 2008. Causation-based T2 decomposition for multivariate process monitoring and diagnosis. Journal of Quality Technology, 40 (1), 46-68.
36. Lv, P., Yuan, L., and Zhang, J.F., 2009. Cloud theory-based simulated annealing algorithm and application. Engineering Applications of Artificial Intelligence, 22 (4-5), 742-749.
37. Mason, R.L., Tracy, N.D., and Yong, J.C., 1995. Decomposition of it T2 for multivariate control chart interpretation. Journal of Quality Technology, 27 (2), 140-143.
38. Mason, R.L., Tracy, N.D., and Yong, J.C., 1997. A practical approach of interpreting multivariate T2 control chart signals. Journal of Quality Technology, 29 (4), 396-406. (Pubitemid 127581421)
39. Montgomery, D.C., 2001. Statistical quality control. 4th ed. New York: Wiley.
40. Niaki, S.T.A. and Abbasi, B., 2005. Fault diagnosis in multivariate control charts using artificial neural networks. Quality and Reliability Engineering International, 21 (8), 825-840. (Pubitemid 41766868)
41. Niaki, S.T.A. and Abbasi, B., 2008. Detection and classification mean-shifts in multi-attribute processes by artificial neural networks. International Journal of Production Research, 46 (11), 2945-2963. (Pubitemid 351499528)
42. Pacella, M., Semeraro, Q., and Anglani, A., 2004. Manufacturing quality control by means of a Fuzzy ART network trained on natural process data. Engineering Applications of Artificial Intelligence, 17 (1), 83-96.
43. Pal, S.K. and Mitra, S., 1992. Multilayer perceptron, fuzzy sets, and classification. IEEE Transaction on Neural Networks, 3 (5), 683-697. (Pubitemid 23555783)
44. Pandey, V., Tiwari, M.K., and Kumar, S., 2006. An interactive approach to solve the operation sequencing problem using simulated annealing. International Journal of Advanced Manufacturing Technology, 29 (11-12), 1212-1231. (Pubitemid 44099800)
45. Pugh, G.A., 1989. Synthetic neural networks for process control. Computer and Industrial Engineering, 17 (1-4), 24-26. (Pubitemid 20627091)
46. Schapire, R.E., 1990. The strength of weak learnability. Machine Learning, 5 (2), 197-227.
47. Smith, A.E., 1994. X-Bar and R control chart interpretation using neural computing. International journal of Production Research, 32 (2), 309-320.
48. Specht, D.F., 1990. Probabilistic neural networks. Neural Networks, 3 (1), 109-118.
49. Sun, R. and Tsung, F., 2003. A kernel-distance-based multivariate control chart using support vector methods. International Journal of Production Research, 41 (13), 2975-2989.
50. Vapnik, V.N., 1995. The nature of statistical learning theory. Berlin: Springer.
51. Vapnik, V.N., 1998. Statistical learning theory. New York: Wiley.
52. Verron, S., Li., J., and Tiplica, T., 2010. Fault detection and isolation of faults in a multivariate process with Bayesian network. Journal of Process Control, 28 (8), 902-911.
53. Woodall, W.H. and Montgomery, D.C., 1999. Research issues and ideas in statistical process control. Journal of Quality Technology, 31 (4), 376-386. (Pubitemid 129585019)
54. Yu, J.B. and Xi, L.F., 2009. A hybrid learning-based model for on-line monitoring and diagnosis of outof- control signals in multivariate manufacturing processes. International Journal of Production Research, 47 (15), 4077-4108.
55. Zhou, Z.H., Wu, J.X., and Tang, W., 2002. Ensembling neural networks: many could be better than all. Artificial Intelligence, 137 (1-2), 239-263. (Pubitemid 34405220)