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dc.contributor.authorZaki, Rezgar
dc.contributor.authorBarabadi, Abbas
dc.contributor.authorGarmabaki, Amir Hossein Soleiman
dc.contributor.authorNuri, Ali
dc.date.accessioned2020-03-27T12:16:46Z
dc.date.available2020-03-27T12:16:46Z
dc.date.issued2019-11-07
dc.description.abstractKnowing the maintainability of a component or a system means that repair resource allocations, such as spare part procurement and maintenance training, can be planned and optimized more effectively. Repair data are often collected from multiple and distributed units in different operational conditions, which can introduce heterogeneity into the data. Part of such heterogeneity can be explained and isolated by the observable covariates, whose values and the way that they can affect the item’s maintainability are known. However, some factors which may affect maintainability are typically unknown (unobserved covariates), leading to unobserved heterogeneity. Nevertheless, many researchers have ignored the effect of observed and un-observed covariates, and this may lead to erroneous model selection, as well as wrong conclusions and decisions. Moreover, many authors have simplified their analysis by considering a complex system as a single item. In these studies, the assumption is that all repair data represent an identical repair process for the item. In practice, mechanical systems are composed of multiple parts, with various failure mechanisms, which need different repair processes (repair modes) to return to the operational phase; classical distribution, such as lognormal, which is only a function of time, may not be able to model such complexity. The paper utilizes the mixture frailty model (MFM) in the presence of some specific observed or unobserved covariates to predict maintainability more precisely. MFMs can model the effect of observed and unobserved covariates, as well as identifying different repair processes in the repair dataset. The application of the proposed model is demonstrated by a case study.en_US
dc.descriptionThis is a post-peer-review, pre-copyedit version of an article published in <i>International Journal of System Assurance Engineering and Management.</i> The final authenticated version is available online at: http://dx.doi.org/10.1007/s13198-019-00917-3.en_US
dc.identifier.citationZaki, R.; Barabadi, A.; Qarahasanlou, A.N.; Garmabaki, A.H.S. (2019) A mixture frailty model for maintainability analysis of mechanical components: a case study.<i> International Journal of Systems Assurance Engineering and Management, 10,</i> (6),1646-1653en_US
dc.identifier.cristinIDFRIDAID 1752061
dc.identifier.doi10.1007/s13198-019-00917-3
dc.identifier.issn0975-6809
dc.identifier.issn0976-4348
dc.identifier.urihttps://hdl.handle.net/10037/17895
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofZaki, R. (2020). Performance Measurement System in complex environment: observed and unobserved risk factors. (Doctoral thesis). <a href=https://hdl.handle.net/10037/19902>https://hdl.handle.net/10037/19902</a>
dc.relation.journalInternational Journal of Systems Assurance Engineering and Management
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2019 Springeren_US
dc.subjectVDP::Technology: 500::Mechanical engineering: 570::Production and maintenance engineering: 572en_US
dc.subjectVDP::Teknologi: 500::Maskinfag: 570::Produksjon og driftsteknologi: 572en_US
dc.titleA mixture frailty model for maintainability analysis of mechanical components: a case studyen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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