Dynamic Bayesian Network for reliability of mechatronic system with taking account the multi-domain interaction

• Autorzy: Amrani Nabil B. , Saintis Laurent , Sarsri Driss , Barreau Mihaela

Identyfikatory

DOI

10.2478/jok-2019-0002

• Języki publikacji: EN

Abstrakty

EN

This article presents a methodology for reliability prediction during the design phase of mechatronic system considered as an interactive dynamic system. The difficulty in modeling reliability of a mechatronic system is mainly due to failures related to the interaction between the different domains called Multi-domain interaction. Therefore in this paper, after a presentation of the state of the art of mechatronic systems reliability estimation methods, we propose a original approach by representing multi domain interactions by influential factors in the dysfunctional modeled by Dynamic Bayesian Networks. A case study demonstrates the interest of the proposed approach.

Słowa kluczowe

EN

predictive reliability; quantitative modeling; dynamic hybrid system; mechatronics; multi-domain interactions; Dynamic Bayesian Network

• Wydawca: Wydawnictwo Instytutu Technicznego Wojsk Lotniczych
• Czasopismo: Journal of KONBiN
• Rocznik: 2019
• Tom: Vol. 49, iss. 1
• Strony: 31--46
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Amrani Nabil B.

• LARIS, Engineering School ISTIA, University of Angers, France
• LTI, National School of Applied Sciences, ENSA-Tangier, UAE, Morocco

autor

Saintis Laurent

• LARIS, Engineering School ISTIA, University of Angers, France

autor

Sarsri Driss

• LTI, National School of Applied Sciences, ENSA-Tangier, UAE, Morocco

autor

Barreau Mihaela

• LARIS, Engineering School ISTIA, University of Angers, France

Bibliografia

• 1. Amrani N.B., Saintis L., Sarsri D., Barreau M.: Evaluating the predicted reliability of mechatronic systems: State of the art. Mechanical Engineering: An International Journal (MEIJ), 3(2) 2016.
• 2. 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 Mu 20, France, Saint-Malo 2016.
• 3. Barreau M., Todoskoff A., Morel A., Guerin F., Mihalache A.: Dependability assessment for mechatronic systems: electronic stability. 5th IFAC Symposium Fault Detection, Supervision and Safety, Safe process, USA, Washington 2003.
• 4. Belhadaoui H.: Conception sûre des systèmes mécatroniques intelligents pour des applications critiques. Automatique. PhD Thesis, Institut National Polytechnique, France, Lorraine 2011.
• 5. Bishop R.H., The mechatronic HANDBOOK. The publishing division of ISA The Instrumentation, Systems, and Automation Society, 2002.
• 6. Bobbio A., Portinale L., Minichino M., Ciancamerla E.: Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliability Engineering and System Safety, 71,3, 2001.
• 7. Deleuze G., Quatrain R., Jouanet F., Talbourdet D., Lucet F.: A Method for the Assessment of Common Cause Failures of Digital I and C Hardware. The annual European Safety and Reliability Conference ESREL, Finland, Helsinki 2012.
• 8. Demri A., Guerin F., Bigaud D.: Mechatronic system reliability evaluation using Petri networks and phi2 method. Proceeding Int. Conf. European Safety and Reliability Conference ESREL, Czech Republic, Prague 2009.
• 9. Habchi G., Barthod C.: An overall methodology for reliability prediction of mechatronic systems design with industrial application. Reliability Engineering and System Safety. Vol. 155(C), DOI: 10.1016/j.ress.2016.06.013.
• 10. Khalfaoui S., Guilhem E.E., Demmou H., Valette R.: A method for deriving critical scenarios in mechatronic systems. Proc. Int. Conf. Lambda mu 13 Europeen Conference on System Dependability and Safety, France, Lyon 2002.
• 11. Langsetha H., Portinaleb L.: Bayesian networks in reliability. Elsivier, Reliability Engineering System Safety, Vol. 92, Iss. 1, 2007.
• 12. Leger A., Weber P., Levrat E., Duval C., Farret R., Iung B.: Methodological developments for probabilistic risk analyses of socio technical systems. Proceedings of the institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. 223(4), 313, 2009, DOI:10.1243/1748006XJRR230.
• 13. Meeker W.Q., Escobar L.A.: A statistical methods for reliability data. Wiley, Quality Productivity and Reliability, New York 1998.
• 14. Mihalache A., Guerin F., Barreau M., Todoskoff A., Bacivarov I.: Reliability building of mechatronic systems. Proc. Int. the IEEE 10th International Conference on Quality and Dependability, Romania, Sinaia 2006.
• 15. Moncelet G., Christensen S., Demmou H.: Analysing a mechatronic system with coloured Petri net. International Journal on Software Tools for Technology Transfer. Springer. 2 2 1998.
• 16. Saintis L., Hugues E., Bes C., Mongeau M.: Computing In-Service Aircraft reliability. International Journal of Reliability Quality and Safety Engineering, 16, 91, 2009.
• 17. Schoenig R., Aubry J., Cambois T., Hutinet T.: An aggregation method of Markov graphs for the reliability analysis of hybrid systems. Reliability Engineering and System Safety. 91, 2006.
• 18. Sharma R.K., Sharma P.: Qualitative and quantitative approaches to analyse reliability of a mechatronic system: A case. Journal of Industrial Engineering International, Springer, Heidelberg, Vol. 11, 2015, DOI 10.1007/s40092-015-0098-6.
• 19. Weber Ph., Joue L.: Reliability modeling with dynamic Bayesian networks. 5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, USA, Washington 2003.
• 20. Wójcicki T.: Use of Bayesian networks and augmented reality to reliability testing of complex technical objects. Journal of KONBiN, Vol. 35, No. 1, 2015.
• 21. Ziegler Ch., Sûreté de fonctionnement d’architectures informatiques embarquées sur automobile. PhD Thesis, Institut National Polytechnique INPT, France, Toulouse 1996.