Premium
Probability and Frequency Derivation Using Dynamic Fault Trees
Author(s) -
Hamaidia Mohyiddine,
Kara Mohammed,
Innal Fares
Publication year - 2018
Publication title -
process safety progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.378
H-Index - 40
eISSN - 1547-5913
pISSN - 1066-8527
DOI - 10.1002/prs.11971
Subject(s) - fault tree analysis , benchmark (surveying) , event (particle physics) , markov chain , event tree , computer science , process (computing) , function (biology) , algorithm , limit (mathematics) , fault (geology) , reliability engineering , tree (set theory) , engineering , mathematics , machine learning , mathematical analysis , physics , geodesy , quantum mechanics , evolutionary biology , seismology , geology , biology , geography , operating system
Dynamic Fault Tree (DFT) is suitable to capture functional and dynamic dependencies among events leading to system failure. There exist several approaches for analyzing DFTs, each of them suffers from drawbacks that limit its practical use. This paper presents a comprehensive methodology based on the structure function determination. It consists in performing both qualitative and quantitative analysis to establish failure sequences and to calculate the likelihood (probability and frequency) of dynamic systems failure. Failure sequences are extracted from the minimal canonical form of the top event and allow getting disjunctive failure sequences. From the quantitative point of view, the probability and frequency of any DFT top event are developed to make the proposed model able to quantify the failure probability and frequency of dynamic systems and the frequency of accident scenarios. The proposed procedure is applied on two benchmark examples: the first one is a safety‐related system while the second one is an accident scenario with dependent protection layers. In order to check the validity of the proposed method, the derived numerical results are compared with those obtained from Markov Chains models. © 2018 American Institute of Chemical Engineers Process Saf Prog 37: 535–552, 2018