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dc.contributor.authorKabir, Sohag
dc.contributor.authorCampean, I. Felician
dc.date.accessioned2021-12-10T17:31:25Z
dc.date.accessioned2021-12-22T16:30:18Z
dc.date.available2021-12-10T17:31:25Z
dc.date.available2021-12-22T16:30:18Z
dc.date.issued2022
dc.identifier.citationKabir S and Campean IF (2022) A Model-Based Reliability Analysis Method Using Bayesian Network. In: Jansen T, Jensen R, Mac Parthaláin N, et al (Eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing. Vol 1409. Springer, Cham.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18710
dc.descriptionYesen_US
dc.description.abstractBayesian Network (BN)-based methods are increasingly used in system reliability analysis. While BNs enable to perform multiple analyses based on a single model, the construction of robust BN models relies either on the conversion from other intermediate system model structures or direct analyst-led development based on experts input, both requiring significant human effort. This article proposes an architecture model-based approach for the direct generation of a BN model. Given the architectural model of a system, a systematic bottom-up approach is suggested, underpinned by failure behaviour models of components composed based on interaction models to create a system-level failure behaviour model. Interoperability and reusability of models are supported by a library of component failure models. The approach was illustrated with application to a case study of a steam boiler system.en_US
dc.language.isoenen_US
dc.publisherSpringer
dc.relation.isreferencedbyhttps://doi.org/10.1007/978-3-030-87094-2_43en_US
dc.subjectModel-based reliability analysisen_US
dc.subjectBayesian networksen_US
dc.subjectReliability analysisen_US
dc.titleA Model-Based Reliability Analysis Method Using Bayesian Networken_US
dc.status.refereedYesen_US
dc.date.Accepted2021-09-10
dc.date.application2021-11-01
dc.typeBook chapteren_US
dc.date.EndofEmbargo2023-10-18
dc.type.versionAccepted manuscripten_US
dc.description.publicnotesThe full text will be available at the end of the publisher's embargo: 18th Nov 2023
dc.date.updated2021-12-10T17:31:27Z
refterms.dateFOA2021-12-22T16:32:17Z
dc.openaccess.statusGreen Open Accessen_US


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