Causal analysis for troubleshooting and decision support system

2011 IEEE International Conference on Prognostics and Health Management, PHM 2011 - Conference Proceedings

Volumn , Issue , 2011, Pages

  Kim, Byoung Uk ^a   Vohnout, Sonia ^a   Mikkola, Esko ^a   Li, Mingyang ^b   Liu, Jian ^b  

^a Ridgetop Group Inc   (United States)

^b University of Arizona   (United States)

Author keywords

Bayesian network; causal analysis; decision support; fault; health management; troubleshooting

Indexed keywords

CAUSAL ANALYSIS; CAUSAL BAYESIAN NETWORK; COMPLEX COMPONENTS; CONDITION-BASED MAINTENANCE; CONSTRAINT-BASED; CRITICAL CHALLENGES; DECISION SUPPORTS; ELECTROMECHANICAL ACTUATOR; FAULT; FLIGHT SAFETY; HEALTH MANAGEMENT; INTEGRATED SYSTEM HEALTH MANAGEMENTS; MAINTENANCE COST; ORIGINAL STRUCTURES; RESEARCH TOPICS; TEST DATA; TROUBLESHOOTS;

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; DIAGNOSIS; ELECTROMECHANICAL DEVICES; HEALTH; MAINTAINABILITY; STATISTICAL TESTS;

DECISION SUPPORT SYSTEMS;

EID: 80053630589     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPHM.2011.6024344     Document Type: Conference Paper

References (27)

1. Watson, H.A.: Launch Control Safety Study, Section VII, vol. 1. Bell Labs, Murray Hill, New Jersey (1961)
2. Pearl, J. (1986). Fusion, propagation, and structuring in belief networks. Artificial Intelligence, 29, 241-288.
3. Pearl J. Probabilistic reasoning in intelligent systems. Los Altos, CA: Morgan Kaufmann, 1989.
4. Neil, M., Fenton, N. E., Forey, S., & Harris, R. (2001). Using Bayesian belief networks to predict the reliability of military vehicles. IEEE Computing & Control Engineering, 12(1), 11-20. (Pubitemid 32252237)
5. Turtle, H. R. (1990). Inference networks for document retrieval. Ph.D. thesis, University of Massachusetts.
6. G. A. Davis (2003). "Bayesian reconstruction of traffic accidents". Law, Probability and Risk 2: 69-89.
7. Fenton, N. E., Marsh, W., Neil, M., Cates, P., Forey, S., & Tailor, M. (2004). Making resource decisions for software projects. Presented at Proceedings of 26th International Conference on Software Engineering (ICSE 2004), Edinburgh, United Kingdom, IEEE Computer Society 2004.
8. Wiboonsak Watthayu, and Yun Peng, A Bayesian network based framework for multi-criteria decision making, Proceedings of the 17th International Conference on Multiple Criteria Decision Analysis Date: August 06, 2004.
9. Knowledge discovery from observational data for process control using causal Bayesian networks, JING LI and JIANJUN SHI, IIE Transactions (2007) 39, 681-690.
10. Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters, Pedro Larrafiaga, Mikel Poza, Yosu Yurramendi, Roberto H. Murga, and Cindy M.H. Kuijpers, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 18, NO. 9, SEPTEMBER 1996.
11. Learning Bayesian Networks for Discrete Data, Faming Liang, Jian Zhang, computational Statistics & Data Analysis Volume 53, Issue 4, 15 February 2009, Pages 865-876.
12. Gran, B. A. (2002). The use of Bayesian Belief Networks for combining disparate sources of information in the safety assessment of software based systems. Ph. D. thesis, Department of Mathematical Sciences, Norwegian University of Science and Technology. Doktor Ingeniør avhandling 2002:35.
13. Jensen, F. V. (2001). Bayesian Networks and Decision Graphs. New York: Springer-Verlag.
14. Bobbio, A., L. Portinale, M. Minichino, and E. Ciancamerla (2001). Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliability Engineering and System Safety 71 (3), 249-260. (Pubitemid 32077203)
15. G. Eason, B. Noble, and I. N. Sneddon, "On certain integrals of Lipschitz-Hankel type involving products of Bessel functions," Phil. Trans. Roy. Soc. London, vol. A247, pp. 529-551, April 1955. (references)
16. Spirtes, P., Glymour, C., Scheines, R., 1993. Causation, Prediction and Search. Springer-Verlag, New York.
17. Wermuth, N., Lauritzen, S., 1983. Graphical and recursive models for contingency tables. Biometrika, 72, 537-552.
18. De Campos, L.M., Huete, J.F., 2000. A new approach for learning belief networks using independence criteria. Int. J. Approx. Reason 24, 11-37. (Pubitemid 34198912)
19. Bouckaert, R.R., 1995. Bayesian belief networks: from construction to inference. Ph.D. Thesis, University of Utrecht.
20. Chen, H.F., 2002. Stochastic Approximation and Its Applications. Kluwer Academic Publishers, Dordrecht.
21. Neil, J.R., Korb, K.B., 1999. The evolution of causal models. In: Zhong, N., Zhous, L.(Eds.), Third Pacific Asia Conference on Knowledge Discovery and Data Mining. Springer-Verlag, pp. 432-437.
22. Pedro Larrafiaga, Mike Poza, Yosu Yurramendi, Roberto H. Murga, and Cindy M.H. Kuijpers, Structure Learning of Bayesian Networks by Genetic Algorithms: Performance Analysis of Control Parameters, IEEETransactions on Pattern Analysis and Machine Intelligence, Vol. 18,No. 9 September, 1996.
23. Madigan, D., York, J., 1995. Bayesian graphical models for discrete data. International Statistical Review, 63, 215-232.
24. J. Pearl. 1988. Probabilistic Reasoning with Intelligent Systems. Morgan & Kaufman, San Mateo.
25. J. Abellan, M. Gomez-Olmedo, and S. Moral, Some Variations on the PC Algorithm, 2006. In Proceedings of the Third European Workshop on Probabilistic Graphical Models (PGM' 06), pages 1-8.
26. M. Kalisch and P. Buhlmann, Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm, Journal of Machine Learning Research 8 (2007) 613-636. (Pubitemid 46473523)
27. Byoung Uk Kim, C. Lynn, N. Kunst, T. Dudgeon "Pattern Analysis in Real Time with Smart Power Sensor", presented in 2010 IEEE Aerospace Conference (IEEE AIAA 2010), Big Sky, Montana, USA, March 2010