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Probabilistic sensitivity analysis of system availability using Gaussian processes

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  • Daneshkhah, Alireza
  • Bedford, Tim

Abstract

The availability of a system under a given failure/repair process is a function of time which can be determined through a set of integral equations and usually calculated numerically. We focus here on the issue of carrying out sensitivity analysis of availability to determine the influence of the input parameters. The main purpose is to study the sensitivity of the system availability with respect to the changes in the main parameters. In the simplest case that the failure repair process is (continuous time/discrete state) Markovian, explicit formulae are well known. Unfortunately, in more general cases availability is often a complicated function of the parameters without closed form solution. Thus, the computation of sensitivity measures would be time-consuming or even infeasible.

Suggested Citation

  • Daneshkhah, Alireza & Bedford, Tim, 2013. "Probabilistic sensitivity analysis of system availability using Gaussian processes," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 82-93.
    • Handle: RePEc:eee:reensy:v:112:y:2013:i:c:p:82-93
    DOI: 10.1016/j.ress.2012.11.001
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    References listed on IDEAS

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    Cited by:

    1. Kondakci, Suleyman, 2015. "Analysis of information security reliability: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 275-299.
    2. Daneshkhah, A. & Stocks, N.G. & Jeffrey, P., 2017. "Probabilistic sensitivity analysis of optimised preventive maintenance strategies for deteriorating infrastructure assets," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 33-45.
    3. Anil Kr. Aggarwal & Sanjeev Kumar & Vikram Singh, 2016. "Mathematical modeling and fuzzy availability analysis of skim milk powder system of a dairy plant," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(1), pages 322-334, December.
    4. Al Ali, Hannah & Daneshkhah, Alireza & Boutayeb, Abdesslam & Malunguza, Noble Jahalamajaha & Mukandavire, Zindoga, 2022. "Exploring dynamical properties of a Type 1 diabetes model using sensitivity approaches," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 324-342.
    5. Hannah Al Ali & Alireza Daneshkhah & Abdesslam Boutayeb & Zindoga Mukandavire, 2022. "Examining Type 1 Diabetes Mathematical Models Using Experimental Data," IJERPH, MDPI, vol. 19(2), pages 1-20, January.
    6. Zitrou, A. & Bedford, T. & Daneshkhah, A., 2013. "Robustness of maintenance decisions: Uncertainty modelling and value of information," Reliability Engineering and System Safety, Elsevier, vol. 120(C), pages 60-71.

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