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PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE
Edited by: J. Pombo
Virtual Maintenance Laboratory (VirMaLab): A Modelling Approach for Optimizing Maintenance Strategies
L. Bouillaut1, P. Aknin1, I. Ayadi1 and S. Bondeux2
1Université Paris-Est, IFSTTAR, GRETTIA, Noisy-le-Grand, France
L. Bouillaut, P. Aknin, I. Ayadi, S. Bondeux, "Virtual Maintenance Laboratory (VirMaLab): A Modelling Approach for Optimizing Maintenance Strategies", in J. Pombo, (Editor), "Proceedings of the First International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 89, 2012. doi:10.4203/ccp.98.89
Keywords: maintenance modelling and optimization, degradation process modelling, Bayesian networks, rail maintenance.
Reliability analysis is an integral part of system design and operating. Moreover, it can be an input to optimize maintenance policies. Recently, dynamic Bayesian networks (DBN) have been proved appropriate to represent complex systems and perform reliability studies. Based on this formalism, the Diagnosis and Maintenance team of IFSTTAR-GRETTIA, proposed a semi-Markovian generic approach (named VirMaLab, for Virtual Maintenance Laboratory), that enables the modelling of the degradation process of complex systems (multi-components, multi-states, eventually influenced by contextual variables), diagnosis device properties (good detection and false alarm rates, etc.) and maintenance actions. Finally, this decision support tool enables evaluation and comparision of maintenance strategies for most industrial systems.
This paper first describes the generic approach considered, in three steps:
The VirMaLab modelling is based on the Bayesian networks formalism, offering many advantages detailed in the paper. As an illustration of the feasibility of the proposed approach, a railway application, dedicated to the optimization of the rail maintenance (compromise renewal-replacement) will be introduced.
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