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Handling Uncertainties in Fault Tree Analysis by a Hybrid Probabilistic–Possibilistic Framework
Author(s) -
Wang Dong,
Zhang Yan,
Jia Xiang,
Jiang Ping,
Guo Bo
Publication year - 2016
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1821
Subject(s) - fault tree analysis , probabilistic logic , possibility theory , fuzzy logic , computer science , fuzzy set , representation (politics) , probability theory , set (abstract data type) , probabilistic risk assessment , uncertainty analysis , data mining , reliability engineering , mathematics , artificial intelligence , engineering , statistics , politics , law , political science , programming language , simulation
Fault tree analysis is a method largely used in probabilistic risk assessment. Uncertainties should be properly handled in fault tree analyses to support a robust decision making. While many sources of uncertainties are considered, dependence uncertainties are not much explored. Such uncertainties can be labeled as ‘epistemic’ because of the way dependence is modeled. In practice, despite probability theory, alternative mathematical structures, including possibility theory and fuzzy set theory, for the representation of epistemic uncertainty can be used. In this article, a fuzzy β factor is considered to represent the failure dependence uncertainties among basic events. The relationship between β factor and system failure probability is analyzed to support the use of a hybrid probabilistic–possibilistic approach. As a result, a complete hybrid probabilistic–possibilistic framework is constructed. A case study of a high integrity pressure protection system is discussed. The results show that the proposed method provides decision makers a more accurate understanding of the system under analysis when failure dependencies are involved. Copyright © 2015 John Wiley & Sons, Ltd.