 Dynamic fault tree analysis based on continuous-time Bayesian networks under fuzzy numbersYan-Feng Li, Jinhua Mi, Yu Liu, Yuan-Jian Yang and Hong-Zhong Huang Journal of Risk and Reliability, 2015, vol. 229, issue 6, 530-541 Abstract: In the calculation of dynamic fault trees, the existing state space–based methods, such as Markov chain method, are basically global-state models, which make the solution procedure very complex. Bayesian networks have become a popular tool to build probability models and conduct inference for reliability design and analysis in various industry fields. The “state explosion†problem can be alleviated by Bayesian networks. Furthermore, to obtain sufficient failure data sets in real engineering systems is extremely difficult and thus causes the parametric uncertainty in failure data. To address these issues, a novel dynamic fault tree analysis method based on the continuous-time Bayesian networks under fuzzy numbers is proposed in this article. The probability distributions under fuzzy numbers for the output variable of dynamic logic gates are determined. The calculation of fuzzy failure probability of a system is presented. Finally, an example is given to demonstrate the effectiveness of the proposed method. Keywords: System reliability; fault tree analysis; dynamic fault tree analysis; Bayesian networks; fuzzy numbers (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:229:y:2015:i:6:p:530-541 Access Statistics for this article More articles in Journal of Risk and Reliability |
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