Efficient Monte Carlo simulation of stochastic hybrid systems

Abstract : This paper proposes an efficient approach to model stochastic hybrid systems and to implement Monte Carlo simulation for such models, thus allowing the calculation of various probabilistic indicators: reliability , availability, average production, life cycle cost etc. Stochastic hybrid systems can be considered, most of the time, as Piecewise Deterministic Markov Processes (PDMP). Although PDMP have been long ago formalized and studied from a theoretical point of view by Davis (Davis 1993), they are still difficult to use in real applications. The solution proposed here relies on a novel method to handle the case when the hazard rate of a transition λ depends on continuous variables of the system model, the use of an extension of Modelica 3.3 and on Monte Carlo simulation. We illustrate the approach with a simple example: a heating system subject to failures, for which we give the details of the modeling and some calculation results. We compare our ideas to other approaches reported in the literature.
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Marc Bouissou, Hilding Elmqvist, Martin Otter, Albert Benveniste. Efficient Monte Carlo simulation of stochastic hybrid systems. The 10th International Modelica Conference 2014, Hubertus Tummescheit; Karl-Erik Årzén, Mar 2014, Lund, Sweden. ⟨10.3384/ECP14096715⟩. ⟨hal-01182410⟩

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