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Generalized Continuous Time Bayesian Networks as a modelling and analysis formalism for dependable systems

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  • Codetta-Raiteri, Daniele
  • Portinale, Luigi

Abstract

We discuss the main features of Generalized Continuous Time Bayesian Networks (GCTBN) as a dependability formalism: we resort to two specific case studies adapted from the literature, and we discuss modelling choices, analysis results and advantages with respect to other formalisms. From the modelling point of view, GTCBN allow the introduction of general probabilistic dependencies and conditional dependencies in state transition rates of system components. From the analysis point of view, any task ascribable to a posterior probability computation can be implemented, among which the computation of system unreliability, importance indices, system monitoring, prediction and diagnosis.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:167:y:2017:i:c:p:639-651
    DOI: 10.1016/j.ress.2017.04.014
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    References listed on IDEAS

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    1. P Weber & D Theilliol & C Aubrun, 2008. "Component reliability in fault-diagnosis decision making based on dynamic Bayesian networks," Journal of Risk and Reliability, , vol. 222(2), pages 161-172, June.
    2. Langseth, Helge & Portinale, Luigi, 2007. "Bayesian networks in reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 92-108.
    3. 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.
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    2. 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.

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