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Asymptotic optimality of RESTART estimators in highly dependable systems

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  • Villén-Altamirano, J.

Abstract

We consider a wide class of models that includes the highly reliable Markovian systems (HRMS) often used to represent the evolution of multi-component systems in reliability settings. Repair times and component lifetimes are random variables that follow a general distribution, and the repair service adopts a priority repair rule based on system failure risk. Since crude simulation has proved to be inefficient for highly-dependable systems, the RESTART method is used for the estimation of steady-state unavailability and other reliability measures. In this method, a number of simulation retrials are performed when the process enters regions of the state space where the chance of occurrence of a rare event (e.g., a system failure) is higher. The main difficulty involved in applying this method is finding a suitable function, called the importance function, to define the regions. In this paper we introduce an importance function which, for unbalanced systems, represents a great improvement over the importance function used in previous papers. We also demonstrate the asymptotic optimality of RESTART estimators in these models. Several examples are presented to show the effectiveness of the new approach, and probabilities up to the order of 10−42 are accurately estimated with little computational effort.

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  • Villén-Altamirano, J., 2014. "Asymptotic optimality of RESTART estimators in highly dependable systems," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 115-124.
    • Handle: RePEc:eee:reensy:v:130:y:2014:i:c:p:115-124
    DOI: 10.1016/j.ress.2014.05.012
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    References listed on IDEAS

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    1. Perwez Shahabuddin, 1994. "Importance Sampling for the Simulation of Highly Reliable Markovian Systems," Management Science, INFORMS, vol. 40(3), pages 333-352, March.
    2. Pierre L’Ecuyer & Bruno Tuffin, 2011. "Approximating zero-variance importance sampling in a reliability setting," Annals of Operations Research, Springer, vol. 189(1), pages 277-297, September.
    3. Cadini, F. & Avram, D. & Pedroni, N. & Zio, E., 2012. "Subset Simulation of a reliability model for radioactive waste repository performance assessment," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 75-83.
    4. Zio, E. & Pedroni, N., 2012. "Monte Carlo simulation-based sensitivity analysis of the model of a thermal–hydraulic passive system," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 90-106.
    5. Morio, Jérôme, 2011. "Non-parametric adaptive importance sampling for the probability estimation of a launcher impact position," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 178-183.
    6. Kaynar, Bahar & Ridder, Ad, 2010. "The cross-entropy method with patching for rare-event simulation of large Markov chains," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1380-1397, December.
    7. Xiao, Gang & Li, Zhizhong & Li, Ting, 2007. "Dependability estimation for non-Markov consecutive-k-out-of-n: F repairable systems by fast simulation," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 293-299.
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    Cited by:

    1. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2016. "Advanced RESTART method for the estimation of the probability of failure of highly reliable hybrid dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 117-126.
    2. Briš, Radim & Byczanski, Petr & Goňo, Radomír & Rusek, Stanislav, 2017. "Discrete maintenance optimization of complex multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 80-89.

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