Bayesian networks as knowledge representation system in domain of reliability engineering

• Autorzy: Kusz A. , Maksym P. , Marciniak A. W.

Warianty tytułu

PL

Sieci bayesowskie jako system reprezentacji wiedzy w dziedzinie inzynierii niezawodnosci

• Języki publikacji: EN

Abstrakty

EN

The paper presents Bayesian Networks (BNs) in the context of methodological requirements for building knowledge representation systems in the domain of reliability engineering. BNs, by their nature, are especially useful as a formal and computable language for modeling stochastic and epistemic uncertainty intrinsically present in conceptualization and reasoning about reliability.

PL

W artykule przedstawiono sieci bayesowskie (BNs) w kontekście wymogów metodologicznych do budowy systemów reprezentacji wiedzy w dziedzinie inżynierii niezawodności. Ze swej natury, sieci bayesowskie, są szczególnie przydatne jako formalny i obliczalny język do modelowania niepewności stochastycznej i epistemicznej, Takie rodzaje niepewności są istotną cechą konceptualizacji i rozumowania o niezawodność.

Słowa kluczowe

EN

reliability model; probabilistic network; Bayesian network; knowledge representation; building; reliability analysis; reliability engineering; block diagram

• Wydawca: -
• Czasopismo: Teka Komisji Motoryzacji i Energetyki Rolnictwa
• Rocznik: 2011
• Tom: 11C
• Opis fizyczny: p.173-180,fig.,ref.

Twórcy

autor

Kusz A.

• Department of Technology Fundamentals, University of Life Sciences in Lublin, Lublin, Poland

autor

Maksym P.

autor

Marciniak A. W.

Bibliografia

• 1. Pearl J. (1986).: Fusion, Propagation, and Structuring in Belief Networks. Artifi cial Intelligence 29(3), 241-288.
• 2. Pearl J. (1988).: Probabilistic Reasoning in Intelligent Systems: Network of Plausible Inference. Morgan Kaufmann.
• 3. Halpern J., Y. (2005).: Reasoning about uncertainty. The MIT Press Cambridge, Massachusetts, London.
• 4. Barlow RE.: Using influence diagrams. In: Clarotti CA, Lindley DV, editors. Accelerated life testing and experts’ opinions in reliability; 1988. p. 145–57.
• 5. Langseth H., Portinale L., 2007.: Bayesian networks in reliability. Reliability Engineering and System Safety 92 (2007) 92–108.
• 6. Martz HF, Waller RA.: Bayesian reliability analysis of complex series/parallel systems of binomial subsystems and components. Technometrics 1990;32(4):407–16.
• 7. Torres-Toledano JG, Sucar LE.: Bayesian networks for reliability analysis of complex systems. Lecture notes in artificial intelligence, vol. 1484. Berlin, Germany: Springer; 1998.p. 195–206.
• 8. Solano-Soto J, Sucar, LE.: A methodology for reliable system design. In: Proceedings of the 4th international conference on industrial and engineering applications of artificial intelligence and expert systems. Lecture notes in artificial intelligence, vol. 2070. Berlin, Germany: Springer; 2001. p. 734–45.
• 9. Portinale L, Bobbio A.: Bayesian networks for dependability analysis: an application to digital control reliability. In: Proceedings of the fifteenth conference on uncertainty in artifi cial intelligence. San Francisco, CA: Morgan Kaufmann Publishers; 1999. p. 551–8.
• 10. Bobbio A, Portinale L, Minichino M, Ciancamerla E.: Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliab Eng Syst Saf 2001;71(3):249– 60.
• 11. Tchangani AP.: Reliability analysis using Bayesian networks. Stud. Inform. Control 2001;10(3):181–8.
• 12. Sowa J., F. (2000).: Knowledge representation, Logical, Philosophical, and Computational Foundations. Brooks/Cole.
• 13. Henrion M. (1986).: Some practical issues in constructing belief networks. In L. N. Kanal, T. S. Levitt, and J. F. Lemmer, editors, Uncertainty in Artificial Intelligence 3, pages 161–173. North-Holland, Amsterdam, 1989.
• 14. Agosta J. M. (1991).: Conditional inter-causally independent node distributions, a property of “noisy-or” models. In Proc. Conf. on Uncertainty in Artificial Intelligence, pp. 9–16, San Francisco, 1991. Morgan Kaufmann.
• 15. Agosta J. M., Gardos T. (2004).: Bayes Network “Smart” Diagnostics. Intel Technology Journal, Volume 8, Issue 4, 2004.
• 16. Oniśko A., Marek J. Druzdzel M. J. Wasyluk H. (2001).: Learning Bayesian network parameters from small data sets: Application of noisy-or gates. International Journal of Approximate Reasoning, 27(2):165–182, 2001.
• 17. Vesely W., E., Goldberg F., F. et al. (1981).: Fault Tree Handbook. Systems and Reliability Research, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commision.
• 18. Poole, D., 2003.: „First-Order Probabilistic Inference.” Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence.
• 19. Laskey K.B. (2007).: MEBN: A Language for First-Order Bayesian Knowledge Bases. Artificial intelligence 172(2-3).
• 20. Haenni R. (et al.) (2010).: Probabilistic Logic and Probabilistic Networks. Springer.