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Approaching dynamic reliability with predictive and diagnostic purposes by exploiting dynamic Bayesian networks

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  • Daniele Codetta-Raiteri
  • Luigi Portinale

Abstract

We talk about dynamic reliability when the reliability parameters of the system, such as the failure rates, vary according to the current state of the system. In this article, several versions of a benchmark on dynamic reliability taken from the literature are examined. Each version deals with particular aspects such as state-dependent failure rates, failure on demand, and repair. In dynamic reliability evaluation, the complete behavior of the system has to be taken into account, instead of the only failure propagation as in fault tree analysis. To this aim, we exploit dynamic Bayesian networks and the software tool RADYBAN ( Reliability Analysis with DYnamic BAyesian Networks ), with the goal of computing the system unreliability. Because of the coherence between the results returned by dynamic Bayesian network analysis and those obtained by means of other methods, together with the possibility to compute diagnostic indices, we propose dynamic Bayesian network and RADYBAN to be a valid approach to dynamic reliability evaluation.

Suggested Citation

  • Daniele Codetta-Raiteri & Luigi Portinale, 2014. "Approaching dynamic reliability with predictive and diagnostic purposes by exploiting dynamic Bayesian networks," Journal of Risk and Reliability, , vol. 228(5), pages 488-503, October.
    • Handle: RePEc:sae:risrel:v:228:y:2014:i:5:p:488-503
    DOI: 10.1177/1748006X14533958
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    References listed on IDEAS

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    1. Marseguerra, M. & Zio, E. & Devooght, J. & Labeau, P.E., 1998. "A concept paper on dynamic reliability via Monte Carlo simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 47(2), pages 371-382.
    2. Langseth, Helge & Portinale, Luigi, 2007. "Bayesian networks in reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 92-108.
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    Cited by:

    1. 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.
    2. Jiang, Tao & Liu, Yu, 2017. "Parameter inference for non-repairable multi-state system reliability models by multi-level observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 3-15.
    3. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.
    4. Codetta-Raiteri, Daniele & Portinale, Luigi, 2017. "Generalized Continuous Time Bayesian Networks as a modelling and analysis formalism for dependable systems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 639-651.

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