On-line alert systems for production plants: A conflict based approach

International Journal of Approximate Reasoning

Volumn 45, Issue 2, 2007, Pages 255-270

  Nielsen, Thomas D ^a   Jensen, Finn V ^a  

^a AALBORG UNIVERSITY   (Denmark)

Author keywords

Alert systems; Bayesian networks; Conflict measure

Indexed keywords

BAYESIAN NETWORKS; CLASSIFICATION (OF INFORMATION); DATA ACQUISITION; FAULT DETECTION; INDUSTRIAL PLANTS; PROJECT MANAGEMENT;

ALERT SYSTEMS; CONFLICT MEASURE; SIMULATED DATA; SYSTEM OPERATION;

ONLINE SYSTEMS;

EID: 34249948354     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2006.06.010     Document Type: Article

References (24)

1. Boyen X., Friedman N., and Koller D. Discovering the hidden structure of complex dynamic systems. In: Laskey K.B., and Prade H. (Eds). Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (1999), Morgan Kaufmann Publishers 91-100
2. J. Cheng, D.A. Bell, W. Liu, Learning belief networks from data: an information theory based approach, in: Proceedings of the Sixth ACM International Conference on Information and Knowledge Management, 1997, pp. 325-331.
3. Cheng J., Greiner R., Kelly J., Bell D., and Liu W. Learning Bayesian networks from data: an information-theory based approach. Artificial Intelligence 137 1-2 (2002) 43-90
4. Chien C.-F., Chen S.-L., and Lin Y.-S. Using Bayesian network for fault location on distribution feeder. IEEE Transactions on Power Delivery 17 3 (2002) 785-793
5. G.F. Cooper, E. Herskovits, A Bayesian method for constructing Bayesian belief networks from databases, in: B.D. D'Ambrosio, P. Smets, P.P. Bonissone (Eds.), Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, 1991, pp. 86-94.
6. Darwiche A. A differential approach to inference in Bayesian networks. Journal of the ACM 50 3 (2003) 280-305
7. Dawid A.P. Applications of a general propagation algorithm for a probabilistic expert system. Statistics and Computing 2 (1992) 25-36
8. J. de Kleer, J. Kurien, Fundamentals of model-based diagnosis, in: Proceedings of the Fifth IFAC Symposium on Fault Detection, Supervision, and Safety of Technical Processes (Safeprocess), 2003, pp. 25-36.
9. Duda R.O., and Hart P.E. Pattern Classification and Scene Analysis (1973), John Wiley & Sons, New York
10. Friedman N., Geiger D., and Goldszmidt M. Bayesian network classifiers. Machine Learning 29 2-3 (1997) 131-163
11. Friedman N., Murphy K., and Russell S. Learning the structure of dynamic probabilistic networks. In: Cooper G.F., and Moral S. (Eds). Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (1998), Morgan Kaufmann Publishers 139-147
12. Ibargüengoytia P.H., Vadera S., and Sucar L.E. A probabilistic model for information and sensor validation. The Computer Journal 49 1 (2006) 113-126
13. Jensen F.V. Bayesian Networks and Decision Graphs (2001), Springer-Verlag, New York. 0-387-95259-4
14. Jensen F.V., Chamberlain B., Nordahl T., and Jensen F. Analysis in hugin of data conflict. Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, 1990. Also published in Uncertainty in AI 6, 519-528 (1991), North-Holland, Amsterdam
15. Kim Y.-G., and Valtorta M. On the detection of conflicts in diagnostic Bayesian networks using abstraction. In: Besnard P., and Hanks S. (Eds). Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (1995), Morgan Kaufmann Publishers 362-367
16. Laskey K.B. Conflict and surprise: heuristics for model revision. In: D'Ambrosio B.D., Smets P., and Bonissone P.P. (Eds). Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (1991), Morgan Kaufmann Publishers 197-204
17. D. Lowd, P. Domingos, Naive Bayes models for probability estimation. in: Proceedings of the Twentysecond International Conference on Machine Learning, 2005, pp. 529-536.
18. Madsen A.L., and Jensen F.V. Lazy propagation: a junction tree inference algorithm based on lazy evaluation. Artificial Intelligence 113 (1999) 203-245
19. Mehranbod N., Soroush M., Piovoso M., and Ogunnaike B.A. Probabilistic model for sensor fault detection and identification. American Institute for Chemical Engineers Journal 49 7 (2003) 1787-1802
20. Pearl J. Probabilistic Reasoning in Intelligent Systems. Representation and Reasoning (1988), Morgan Kaufmann Publishers, San Mateo California. 0-934613-73-7
21. Reiter R. A theory of diagnosis from first principles. Artificial Intelligence 32 1 (1987) 57-95
22. V. Romero, R. Rumí, A. Salmerón, Structural learning of Bayesian networks with mixtures of truncated exponentials, in: Proceedings of the Second European Workshop on Probabilistic Graphical Models, 2004, pp. 177-184.
23. Witten I.H., and Frank E. Data Mining: Practical Machine Learning Tools with Java Implementations (2000), Morgan Kaufmann, San Francisco version 3.4.3
24. Yamanishi K., and TakeUchi J.-I. A unifying framework for detecting outliers and change points from non-stationary time series data. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2002), ACM Press 676-681