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dc.contributor.authorDao, Cuong D.
dc.contributor.authorZuo, M.J.
dc.date.accessioned2020-08-06T15:59:50Z
dc.date.accessioned2020-08-17T14:25:50Z
dc.date.available2020-08-06T15:59:50Z
dc.date.available2020-08-17T14:25:50Z
dc.date.issued2017-10
dc.identifier.citationDao CD and Zuo MJ (2017) Optimal selective maintenance for multi-state systems in variable loading conditions. Reliability Engineering and System Safety. 166: 171-180.en_US
dc.identifier.urihttp://hdl.handle.net/10454/17973
dc.descriptionNoen_US
dc.description.abstractThis paper studies the selective maintenance problem for multi-state series systems working in variable loading conditions in the next mission. In the mission, a component's degradation depends on its current state and the load applied on it. A load-dependent degradation model is proposed for multi-state components operating in variable loading conditions. This model is inspired by the load-sharing model where many components share a common workload and the failure rate of a component depends on the state of other components. A Monte-Carlo simulation method is presented to simulate the multi-state component's degradation and to evaluate the system reliability. The final objective is to determine the best selective maintenance strategy to maximize the expected system reliability in the next mission within available resources. An illustrative example, reliability estimation results, and analysis of optimal selective maintenance scenarios for different levels of budget limitation are provided.en_US
dc.language.isoenen_US
dc.subjectDegradation rateen_US
dc.subjectMonte-Carlo simulationen_US
dc.subjectMulti-state systemen_US
dc.subjectReliabilityen_US
dc.subjectSelective maintenanceen_US
dc.subjectVariable operating conditionsen_US
dc.titleOptimal selective maintenance for multi-state systems in variable loading conditionsen_US
dc.status.refereedYesen_US
dc.date.Accepted2016-11-17
dc.date.application2016-11-17
dc.typeArticleen_US
dc.type.versionNo full-text in the repositoryen_US
dc.identifier.doihttps://doi.org/10.1016/j.ress.2016.11.006
dc.date.updated2020-08-06T14:59:51Z


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