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Importance and Sensitivity Analysis in Dynamic Reliability

Author

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  • Robert Eymard

    (Université Paris-Est)

  • Sophie Mercier

    (Université Paris-Est)

  • Michel Roussignol

    (Université Paris-Est)

Abstract

In dynamic reliability, the evolution of a system is governed by a piecewise deterministic Markov process, which is characterized by different input data. Assuming such data to depend on some parameter p ∈ P, our aim is to compute the first-order derivative with respect to each p ∈ P of some functionals of the process, which may help to rank input data according to their relative importance, in view of sensitivity analysis. The functionals of interest are expected values of some function of the process, cumulated on some finite time interval [0,t], and their asymptotic values per unit time. Typical quantities of interest hence are cumulated (production) availability, or mean number of failures on some finite time interval and similar asymptotic quantities. The computation of the first-order derivative with respect to p ∈ P is made through a probabilistic counterpart of the adjoint state method, from the numerical analysis field. Examples are provided, showing the good efficiency of this method, especially in case of a large P.

Suggested Citation

  • Robert Eymard & Sophie Mercier & Michel Roussignol, 2011. "Importance and Sensitivity Analysis in Dynamic Reliability," Methodology and Computing in Applied Probability, Springer, vol. 13(1), pages 75-104, March.
    • Handle: RePEc:spr:metcap:v:13:y:2011:i:1:d:10.1007_s11009-009-9122-x
    DOI: 10.1007/s11009-009-9122-x
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    References listed on IDEAS

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    1. Chiquet, Julien & Limnios, Nikolaos, 2008. "A method to compute the transition function of a piecewise deterministic Markov process with application to reliability," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1397-1403, September.
    2. Konstantopoulos, Takis & Last, Günter, 1999. "On the use of Lyapunov function methods in renewal theory," Stochastic Processes and their Applications, Elsevier, vol. 79(1), pages 165-178, January.
    3. Mercier, Sophie, 2007. "Discrete random bounds for general random variables and applications to reliability," European Journal of Operational Research, Elsevier, vol. 177(1), pages 378-405, February.
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    Cited by:

    1. Matieyendou Lamboni, 2023. "On Exact Distribution for Multivariate Weighted Distributions and Classification," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-26, March.

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