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dc.contributor.authorKabir, Sohag
dc.contributor.authorGoek, T.K.
dc.contributor.authorKumar, M.
dc.contributor.authorYazdi, M.
dc.contributor.authorHossain, F.
dc.date.accessioned2020-08-04T20:14:05Z
dc.date.accessioned2020-08-24T13:48:05Z
dc.date.available2020-08-04T20:14:05Z
dc.date.available2020-08-24T13:48:05Z
dc.date.issued2020
dc.identifier.citationKabir S, Goek TK, Kumar M et al (2020) A method for temporal fault tree analysis using intuitionistic fuzzy set and expert elicitation. IEEE Access. 8: 980-996.en_US
dc.identifier.urihttp://hdl.handle.net/10454/17992
dc.descriptionYesen_US
dc.description.abstractTemporal fault trees (TFTs), an extension of classical Boolean fault trees, can model time-dependent failure behaviour of dynamic systems. The methodologies used for quantitative analysis of TFTs include algebraic solutions, Petri nets (PN), and Bayesian networks (BN). In these approaches, precise failure data of components are usually used to calculate the probability of the top event of a TFT. However, it can be problematic to obtain these precise data due to the imprecise and incomplete information about the components of a system. In this paper, we propose a framework that combines intuitionistic fuzzy set theory and expert elicitation to enable quantitative analysis of TFTs of dynamic systems with uncertain data. Experts’ opinions are taken into account to compute the failure probability of the basic events of the TFT as intuitionistic fuzzy numbers. Subsequently, for the algebraic approach, the intuitionistic fuzzy operators for the logic gates of TFT are defined to quantify the TFT. On the other hand, for the quantification of TFTs via PN and BN-based approaches, the intuitionistic fuzzy numbers are defuzzified to be used in these approaches. As a result, the framework can be used with all the currently available TFT analysis approaches. The effectiveness of the proposed framework is illustrated via application to a practical system and through a comparison of the results of each approach.en_US
dc.description.sponsorshipThis work was supported in part by the Mobile IOT: Location Aware project (grant no. MMUE/180025) and Indoor Internet of Things (IOT) Tracking Algorithm Development based on Radio Signal Characterisation project (grant no. FRGS/1/2018/TK08/MMU/02/1). This research also received partial support from DEIS H2020 project (grant no. 732242).en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1109/ACCESS.2019.2961953en_US
dc.rights© 2020 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. http://creativecommons.org/licenses/by/4.0/en_US
dc.subjectFault tree analysisen_US
dc.subjectReliability analysisen_US
dc.subjectFuzzy seten_US
dc.subjectIntuitionistic fuzzy set theoryen_US
dc.subjectExpert judgementen_US
dc.subjectTemporal fault treesen_US
dc.titleA method for temporal fault tree analysis using intuitionistic fuzzy set and expert elicitationen_US
dc.status.refereedYesen_US
dc.date.Accepted2019-12-18
dc.date.application2019-12-24
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.date.updated2020-08-04T19:14:06Z
refterms.dateFOA2020-08-24T13:48:58Z


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