Bayesian estimation for reliability engineering: Addressing the influence of prior choice

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorLeoni, Leonardoen_US
dc.contributor.authorBahootoroody, Farshaden_US
dc.contributor.authorKhalaj, Saeeden_US
dc.contributor.authorDe Carlo, Filippoen_US
dc.contributor.authorBahootoroody, Ahmaden_US
dc.contributor.authorAbaei, Mohammad Mahdien_US
dc.contributor.departmentDepartment of Mechanical Engineeringen
dc.contributor.groupauthorMarine Technologyen
dc.contributor.organizationUniversity of Florenceen_US
dc.contributor.organizationUniversity of Parsianen_US
dc.contributor.organizationDelft University of Technologyen_US
dc.date.accessioned2021-03-31T06:14:39Z
dc.date.available2021-03-31T06:14:39Z
dc.date.issued2021-03-24en_US
dc.description.abstractOver the last few decades, reliability analysis has attracted significant interest due to its importance in risk and asset integrity management. Meanwhile, Bayesian inference has proven its advantages over other statistical tools, such as maximum likelihood estimation (MLE) and least square estimation (LSE), in estimating the parameters characterizing failure modelling. Indeed, Bayesian inference can incorporate prior beliefs and information into the analysis, which could partially overcome the lack of data. Accordingly, this paper aims to provide a closed-mathematical representation of Bayesian analysis for reliability assessment of industrial components while investigating the effect of the prior choice on future failures predictions. To this end, hierarchical Bayesian modelling (HBM) was tested on three samples with distinct sizes, while five different prior distributions were considered. Moreover, a beta-binomial distribution was adopted to represent the failure behavior of the considered device. The results show that choosing strong informative priors leads to distinct predictions, even if a larger sample size is considered. The outcome of this research could help maintenance engineers and asset managers in integrating their prior beliefs into the reliability estimation process.en
dc.description.versionPeer revieweden
dc.format.extent16
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationLeoni, L, Bahootoroody, F, Khalaj, S, De Carlo, F, Bahootoroody, A & Abaei, M M 2021, ' Bayesian estimation for reliability engineering : Addressing the influence of prior choice ', International Journal of Environmental Research and Public Health, vol. 18, no. 7, 3349 . https://doi.org/10.3390/ijerph18073349en
dc.identifier.doi10.3390/ijerph18073349en_US
dc.identifier.issn1661-7827
dc.identifier.otherPURE UUID: 6e67997d-354e-4064-aac6-46da84fe6c7fen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/6e67997d-354e-4064-aac6-46da84fe6c7fen_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85102865406&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/61429384/ENG_Leoni_et_al_Bayesian_Estimation_for_Reliability_International_Journal_of_Environmental_Research_and_Public_Health.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/103424
dc.identifier.urnURN:NBN:fi:aalto-202103312697
dc.language.isoenen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofseriesInternational Journal of Environmental Research and Public Healthen
dc.relation.ispartofseriesVolume 18, issue 7en
dc.rightsopenAccessen
dc.subject.keywordBeta-binomial failure modellingen_US
dc.subject.keywordHierarchical Bayesian modellingen_US
dc.subject.keywordPrior informationen_US
dc.subject.keywordReliability analysisen_US
dc.titleBayesian estimation for reliability engineering: Addressing the influence of prior choiceen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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