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Evidential Networks for evaluating predictive reliability of mechatronics systems under epistemic uncertainties

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In reliability predicting field, the probabilistic approaches are based on data relating to the components which can be precisely known and validated by the return of experience REX, but in the case of complex systems with high-reliability precision such as mechatronic systems, uncertainties are inevitable and must be considered in order to predict with a degree of confidence the evaluated reliability. In this paper, firstly we present a brief review of the non-probabilistic approaches. Thereafter we present our methodology for assessing the reliability of the mechatronic system by taking into account the epistemic uncertainties (uncertainties in the reliability model and uncertainties in the reliability parameters) considered as a dynamic hybrid system and characterized by the existence of multi-domain interaction between its failed components. The key point in this study is to use an Evidential Network "EN" based on belief functions and the dynamic Bayesian network. Finally, an application is developed to illustrate the interest of the proposed methodology.
Czasopismo
Rocznik
Strony
459--475
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
  • LARIS, Polytech Angers, University of Angers, France
  • LTI, National School of Applied Sciences, ENSA-Tangier, UAE, Morocco
  • LARIS, Polytech Angers, University of Angers, France
autor
  • LTI, National School of Applied Sciences, ENSA-Tangier, UAE, Morocco
  • LTI, National School of Applied Sciences, ENSA-Tangier, UAE, Morocco
Bibliografia
  • 1. Oberkampf, W.L., Helton J.C., Joslyn C.A., Wojtkiewicz S.F., Ferson S.: Challenge Problems: Uncertainty in System Response Given Uncertain Parameters, Reliability Engineering and System Safety, vol. 85, no. 1-3, 2004.
  • 2. Utkin L.V., Coolen F.: Imprecise reliability: An introductory overview. In: Intelligence in Reliability Engineering. Ed. by G. Levitin. Springer Berlin Heidelberg, 2007.
  • 3. Coit D., Jin T., Wattanapong Sakorn N.: System optimization with Component Reliability estimation uncertainty: A multi-criteria Approach, IEEE Transactions on Reliability, 53(2004).
  • 4. Sallak M., Schon W., Felipe A.: The Transferable Belief Model for reliability analysis of systems with data uncertainties and failure dependencies. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, SAGE Publications.224,4, 2010.
  • 5. Tu Duong D., Vasseur TD., Dieulle D.: Representation of parameter uncertainty with evidence theory in Probabilistic Risk Assessment. In Proceeding of the workshop on the theory of belief function, France, Brest, 2010.
  • 6. Simon C., Weber Ph., Evsuko A.: Bayesian networks inference algorithm to implementDempster Shafer theory in reliability analysis. Reliability Engineering and System Safety, elsevier.93, 2008.
  • 7. Weber Ph., Simon C.: Imprecise reliability by evidential networks. Proceedings of the Institution of Mechanical Engineers Part, Journal of Risk and Reliability, SAGE Publications,223, 2009.
  • 8. Honda K., Washizaki H., Fukazawa Y.: Generalized Software Reliability Model Considering Uncertainty and Dynamics: Model and Applications. International Journal of Software Engineering. World scientific Publishing. 27, 2017.
  • 9. Felipe Aguirre et al.: Application of Evidential Networks in quantitative analysis of rail way accidents. Journal of Risk and Reliability - Special issue, 2013.
  • 10. Kochs H.D., Kongniratsaikul P.: Comparing System Reliability Considering Insufficient Knowledge: Application to HVDC Converter Stations, Proceedings of IEEE Power Energy Society General Meeting (IEEE PES GM), July 2012, San Diego, CA USA, 2012.
  • 11. Heard A., Pensky M.: Confidence Intervals for Reliability and Quantile Functions With Application to NASA Space Flight Data, IEEE Transactions on Reliability, Volume 55, Issue 4, Dec. 2006.
  • 12. Kongniratsaikul P.: Evaluation of System Reliability Using the Example of HVDC ystems, Master Thesis, University of Duisburg-Essen, Germany, 2009.
  • 13. Simon Ch., Weber P.: Evidential Networks for Reliability Analysis and Performance Evaluation of Systems With Imprecise Knowledge. In: IEEE Transactions on Reliability 58.1, Mar. 2009.
  • 14. Glenn Shafer. Perspectives on the theory and practice of belief functions. International Journal of Approximate Reasoning.3, 1990.
  • 15. Limbourg Ph., Savic R., Petersen J., Kochs H.-D.: Modelling uncertainty in fault tree analyses using evidence theory. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 222.3 Sept. 2008.
  • 16. Le Duy, Tu Duong: Traitement des incertitudes dans les applications des Etudes Probabilistes de Surete Nucleaire. PhD thesis. Troyes, France: University of Technology of Troyes, Dec. 2011.
  • 17. Xiaomin You, Tonon F.: Event-tree analysis with imprecise probabilities. In: Risk analysis: an official publication of the Society for Risk Analysis 32.2.
  • 18. Zhong X., Ichchou M., Saidi A.: Reliability assessment of complex mechatronic systems using a modified nonparametric belief propagation algorithm. Reliability Engineering and System Safety; 2010, DOI:10.1016/j.ress.2010.05.004.
  • 19. Amrani N.B., Saintis L., Sarsri D., Barreau M.: Bayesian Reliability Model for mechatronic system by taking into account the multi-domain interaction. Journal of KONBIN, Vol. 49, 2019, DOI 10.2478/jok-2019-0002.
  • 20. Amrani N.B., Saintis L., Sarsri D., Barreau M.: Prise en compte des interactions multi-domaines lors de l’évaluation de la fiabilité prévisionnelle des systèmes mécatroniques. Proc. Int. Lambda-mu20. France, Saint Malo, 2016.
  • 21. Habchi G., Barthod C.: An overall methodology for reliability prediction of mechatronic systems design with industrial application. Reliability Engineering and System Safety.155, 2016.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-66aa08d6-54e3-47bc-9fb2-445f853290d7
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