Affiliations: [a] Laboratoire de recherche LR-Automatique, École Nationale d’Ingénieurs de Tunis, Tunis, Tunisie | [b] Unité de recherche ATSI, École Nationale d’Ingénieurs de Monastir, Monastir, Tunisie
Correspondence:
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Corresponding author. Walid Mechri, Laboratoire de recherche LR-Automatique, École Nationale d’Ingénieurs de Tunis, BP37 Le Belvédère, 1002 Tunis, Tunisie. Tel.: +21652250250; Fax: +21673683399; E-mail: walid.mechri@isim.rnu.tn.
Abstract: This paper proposes a method to analyse the uncertainty problem in assessing of the safety systems performance. The method is based on Bayesian networks and integrates several parameters like the factor of Common Cause Failure. The imperfect knowledge concerns the CCF factors involved in the safety system. The point-valued CCF factors are replaced by triangular fuzzy numbers, allowing experts to express their uncertainty about the CCF values. The proposed method shows how the uncertainties of CCF factors propagate through the Bayesian networks and how this induces an uncertainty to the values of the safety system performance. The proposed method ensures the relevance of the results. This is validated by a comparison with the results of probabilistic analysis of a Monte Carlo sampling, where we consider triangular probability distribution of common cause failures factors.
Keywords: Safety systems, probability of failure on demand, Bayesian networks, common cause failure, uncertainty, fuzzy numbers
