METHODOLOGY FOR FLEET UNCERTAINTY REDUCTION WITH UNSUPERVISED LEARNING

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dc.contributor.advisorFUGE, MARK Den_US
dc.contributor.authorModarres, Ceenaen_US
dc.contributor.departmentReliability Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2017-06-22T05:41:25Z
dc.date.available2017-06-22T05:41:25Z
dc.date.issued2016en_US
dc.description.abstractOperational and environmental variance can skew reliability metrics and increase uncertainty around lifetime estimates. For this reason, fleet-wide analysis is often too general for accurate predictions on heterogeneous populations. Also, modern sensor based reliability and maintainability field and test data provide a higher level of specialization and disaggregation to relevant integrity metrics (e.g., amount of damage, remaining useful life). Modern advances, like Dynamic Bayesian Networks, reduce uncertainty on a unit-by-unit basis to apply condition-based maintenance. This thesis presents a methodology for leveraging covariate information to identify sub- populations. This population segmentation based methodology reduces fleet uncertainty for more practical resource allocation and scheduled maintenance. First, the author proposes, validates, and demonstrates a clustering based methodology. Afterwards, the author proposes the application of the Student-T Mixture Model (SMM) within the methodology as a versatile tool for modeling fleets with unclear sub-population boundaries. SMM’s fully Bayesian formulation, which is approximated with Variational Bayes (VB), is motivated and discussed. The scope of this research includes a new modeling approach, a proposed algorithm, and example applications.en_US
dc.identifierhttps://doi.org/10.13016/M2887J
dc.identifier.urihttp://hdl.handle.net/1903/19304
dc.language.isoenen_US
dc.subject.pqcontrolledEngineeringen_US
dc.subject.pquncontrolledCLUSTERINGen_US
dc.subject.pquncontrolledRELIABILITY ENGINEERINGen_US
dc.subject.pquncontrolledSTUDENT-T MIXTURE MODELSen_US
dc.subject.pquncontrolledUNCERTAINTYen_US
dc.subject.pquncontrolledUNSUPERVISED LEARNINGen_US
dc.subject.pquncontrolledVARIATIONAL BAYESen_US
dc.titleMETHODOLOGY FOR FLEET UNCERTAINTY REDUCTION WITH UNSUPERVISED LEARNINGen_US
dc.typeThesisen_US

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