Fault diagnosis of industrial systems with bayesian networks and mutual information

2007 European Control Conference, ECC 2007

Volumn , Issue , 2007, Pages 2304-2311

  Verron, Sylvain ^a   Tiplica, Teodor ^a   Kobi, Abdessamad ^a  

^a UNIVERSITÉ D'ANGERS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CONTROL ENGINEERING;

COMPLEX PROCESSES; INDUSTRIAL PROCESSS; INDUSTRIAL SYSTEMS; MUTUAL INFORMATIONS; RECOGNITION ERROR; TENNESSEE EASTMAN PROCESS;

BAYESIAN NETWORKS;

EID: 84927720611     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.23919/ecc.2007.7068252     Document Type: Conference Paper

References (36)

1. D. C. Montgomery, Introduction to Statistical Quality Control, Third Edition. John Wiley and Sons, 1997.
2. L. H. Chiang, E. L. Russell, and R. D. Braatz, Fault detection and diagnosis in industrial systems. New York: Springer-Verlag, 2001.
3. W. A. Shewhart, Economic control of quality of manufactured product. New York: D. Van Nostrand Co., 1931.
4. S. W. Roberts, "Control chart tests based on geometric moving averages," Technometrics, vol. 1, pp. 239-250, Aot 1959.
5. H. Hotelling, "Multivariate quality control," Techniques of Statistical Analysis, vol., pp. 111-184, 1947.
6. J. Westerhuis, S. Gurden, and A. Smilde, "Standardized q-statistic for improved sensitivity in the monitoring of residuals in mspc," Journal of Chemometrics, vol. 14, no. 4, pp. 335-349, 2000.
7. E. J. Jackson, "Multivariate quality control," Communication Statistics-Theory and Methods, vol. 14, pp. 2657-2688, 1985.
8. B. R. Bakshi, "Multiscale PCA with application to multivariate statistical process monitoring," AIChE Journal, vol. 44, pp. 1596-1610, 1998.
9. M. Kano, K. Nagao, S. Hasebe, I. Hashimoto, H. Ohno, R. Strauss, and B. Bakshi, "Comparison of multivariate statistical process monitoring methods with applications to the eastman challenge problem," Computers and Chemical Engineering, vol. 26, no. 2, pp. 161-174, 2002.
10. R. L. Mason, N. D. Tracy, and J. C. Young, "Decomposition of T2 for multivariate control chart interpretation," Journal of Quality Technology, vol. 27, no. 2, pp. 99-108, 1995.
11. R. Mason, N. Tracy, and J. Young, "A practical approach for interpreting multivariate t2 control chart signals," Journal of Quality Technology, vol. 29, no. 4, pp. 396-406, 1997.
12. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, 1988.
13. E. Charniak, "Bayesian networks without tears," AI Magazine, vol. 12, no. 4, pp. 50-63, 1991.
14. F. V. Jensen, An introduction to Bayesian Networks. Taylor and Francis, London, United Kingdom, 1996.
15. P. Langley, W. Iba, and K. Thompson, "An analysis of bayesian classifiers," in National Conference on Artificial Intelligence, 1992.
16. N. Friedman, D. Geiger, and M. Goldszmidt, "Bayesian network classifiers," Machine Learning, vol. 29, no. 2-3, pp. 131-163, 1997.
17. P. Domingos and M. J. Pazzani, "Beyond independence: Conditions for the optimality of the simple bayesian classifier," in International Conference on Machine Learning, 1996.
18. I. Inza, P. Larranaga, B. Sierra, R. Etxeberria, J. Lozano, and J. Pena, "Representing the behaviour of supervised classification learning algorithms by bayesian networks," Pattern Recognition Letters, vol. 20, no. 11-13, pp. 1201-1209, 1999.
19. Y. Yang and G. I. Webb, "Discretization for naive-bayes learning: Managing discretization bias and variance," Tech. Rep. 131, School of Computer Science and Software Engineering, Monash University, 2003.
20. C. Chow and C. Liu, "Approximating discrete probability distributions with dependence trees," Information Theory, IEEE Transactions on, vol. 14, no. 3, pp. 462-467, 1968.
21. M. Sahami, "Learning limited dependence bayesian classifiers," in Second International Conference on Knowledge Discovery in Databases, 1996.
22. D. Geiger and D. Heckerman, "Knowledge representation and inference in similarity networks and bayesian multinets," Artificial Intelligence, vol. 82, no. 1-2, pp. 45-74, 1996.
23. I. Kononenko, "Semi-naive bayesian classifier," in EWSL-91: Proceedings of the European working session on learning on Machine learning, pp. 206-219, 1991.
24. C. E. Shannon, "A mathematical theory of communication," Bell Sys. Tech. J., vol. 27, pp. 379-423, 623-656, 1948.
25. A. Perez, P. Larranaga, and I. Inza, "Supervised classification with conditional gaussian networks: Increasing the structure complexity from naive bayes," International Journal of Approximate Reasoning, vol. 43, pp. 1-25, 2006.
26. T. M. Cover, Learning in pattern recognition. NY: Methodologies of Pattern Recognition, s. watanabe (ed.) ed., 1969.
27. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification 2nd edition. Wiley, 2001.
28. J. Downs and E. Vogel, "Plant-wide industrial process control problem," Computers and Chemical Engineering, vol. 17, no. 3, pp. 245-255, 1993.
29. N. Ricker, "Decentralized control of the tennessee eastman challenge process," Journal of Process Control, vol. 6, no. 4, pp. 205-221, 1996.
30. J.-M. Lee, C. Yoo, and I.-B. Lee, "Statistical monitoring of dynamic processes based on dynamic independent component analysis," Chemical Engineering Science, vol. 59, no. 14, pp. 2995-3006, 2004.
31. U. Kruger, Y. Zhou, and G. Irwin, "Improved principal component monitoring of large-scale processes," Journal of Process Control, vol. 14, no. 8, pp. 879-888, 2004.
32. L. Chiang, E. Russell, and R. Braatz, "Fault diagnosis in chemical processes using fisher discriminant analysis, discriminant partial least squares, and principal component analysis," Chemometrics and Intelligent Laboratory Systems, vol. 50, no. 2, pp. 243-252, 2000.
33. L. Chiang, M. Kotanchek, and A. Kordon, "Fault diagnosis based on fisher discriminant analysis and support vector machines," Computers and Chemical Engineering, vol. 28, no. 8, pp. 1389-1401, 2004.
34. A. Kulkarni, V. Jayaraman, and B. Kulkarni, "Knowledge incorporated support vector machines to detect faults in tennessee eastman process," Computers and Chemical Engineering, vol. 29, no. 10, pp. 2128-2133, 2005.
35. P. Lyman and C. Georgakis, "Plant-wide control of the tennessee eastman problem," Computers and Chemical Engineering, vol. 19, no. 3, pp. 321-331, 1995.
36. K. P. Murphy, "The bayes net toolbox for matlab," in In Computing Science and Statistics: Proceedings of Interface, 2001.