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dc.contributor.authorKevorkian, Christopher Georgeen
dc.date.accessioned2016-09-28T08:00:31Zen
dc.date.available2016-09-28T08:00:31Zen
dc.date.issued2016-09-27en
dc.identifier.othervt_gsexam:8776en
dc.identifier.urihttp://hdl.handle.net/10919/73047en
dc.description.abstractSmall Unmanned Aerial Vehicles (SUAVs) are rapidly being adopted in the National Airspace (NAS) but experience a much higher failure rate than traditional aircraft. These SUAVs are quickly becoming complex enough to investigate alternative methods of failure analysis. This thesis proposes a method of expanding on the Fault Tree Analysis (FTA) method to a Bayesian Belief Network (BBN) model. FTA is demonstrated to be a special case of BBN and BBN can allow for more complex interactions between nodes than is allowed by FTA. A model can be investigated to determine the components to which failure is most sensitive and allow for redundancies or mitigations against those failures. The introduced method is then applied to the Virginia Tech ESPAARO SUAV.en
dc.format.mediumETDen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectUASen
dc.subjectReliabilityen
dc.subjectBayesianen
dc.subjectFault Tree Analysisen
dc.titleUAS Risk Analysis using Bayesian Belief Networks: An Application to the VirginiaTech ESPAAROen
dc.typeThesisen
dc.contributor.departmentAerospace and Ocean Engineeringen
dc.description.degreeMaster of Scienceen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelmastersen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineAerospace Engineeringen
dc.contributor.committeechairWoolsey, Craig A.en
dc.contributor.committeechairLuxhoj, James T.en
dc.contributor.committeememberRaj, Pradeepen


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