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Piecewise deterministic Markov processes and dynamic reliability

Author

Listed:
  • H Zhang
  • F Dufour
  • Y Dutuit
  • K Gonzalez

Abstract

If the reliability community remains interested in dynamic reliability theory, it is not really convinced by the ability of already available approaches to treating current problems from within the operational domain, even if the methodological quality of these approaches is undeniable. This paper is in keeping with two papers presented in earlier conferences. Its aim is to show the potentialities of a method that combines the high modelling capacity of the piecewise-deterministic processes with the great computing power inherent in the Monte Carlo simulation. This method has been applied to a well-known test-case example to test its ability to solve common dynamic reliability problems. Two sets of results have been obtained. The first one has been compared to those coming from a Petri-net model to obtain a preliminary validation of the proposed method. The second one, related to a more complex case, has been compared to already published results found in the literature. Contrary to already existing methods, the approach here is an exact Monte Carlo sampling method; it does not need time-space discretization.

Suggested Citation

  • H Zhang & F Dufour & Y Dutuit & K Gonzalez, 2008. "Piecewise deterministic Markov processes and dynamic reliability," Journal of Risk and Reliability, , vol. 222(4), pages 545-551, December.
    • Handle: RePEc:sae:risrel:v:222:y:2008:i:4:p:545-551
    DOI: 10.1243/1748006XJRR181
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    Cited by:

    1. Li, Jinghui & Mosleh, Ali & Kang, Rui, 2011. "Likelihood ratio gradient estimation for dynamic reliability applications," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1667-1679.
    2. Signoret, Jean-Pierre & Dutuit, Yves & Cacheux, Pierre-Joseph & Folleau, Cyrille & Collas, Stéphane & Thomas, Philippe, 2013. "Make your Petri nets understandable: Reliability block diagrams driven Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 61-75.
    3. Ferdinando Chiacchio & Fabio Famoso & Diego D’Urso & Sebastian Brusca & Jose Ignacio Aizpurua & Luca Cedola, 2018. "Dynamic Performance Evaluation of Photovoltaic Power Plant by Stochastic Hybrid Fault Tree Automaton Model," Energies, MDPI, vol. 11(2), pages 1-22, January.
    4. Chiacchio, F. & D’Urso, D. & Manno, G. & Compagno, L., 2016. "Stochastic hybrid automaton model of a multi-state system with aging: Reliability assessment and design consequences," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 1-13.
    5. de Saporta, Benoîte & Zhang, Huilong, 2013. "Predictive maintenance for the heated hold-up tank," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 82-90.
    6. Brissaud, Florent & Smidts, Carol & Barros, Anne & Bérenguer, Christophe, 2011. "Dynamic reliability of digital-based transmitters," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 793-813.
    7. Babykina, Génia & Brînzei, Nicolae & Aubry, Jean-François & Deleuze, Gilles, 2016. "Modeling and simulation of a controlled steam generator in the context of dynamic reliability using a Stochastic Hybrid Automaton," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 115-136.

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