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dc.contributor.advisorGüvenir, H. Altayen_US
dc.contributor.authorÖzbek, Serenen_US
dc.date.accessioned2019-06-14T14:05:39Z
dc.date.available2019-06-14T14:05:39Z
dc.date.copyright2019-05
dc.date.issued2019-06
dc.date.submitted2019-06-14
dc.identifier.urihttp://hdl.handle.net/11693/52053
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2019.en_US
dc.descriptionIncludes bibliographical references (leaves 52-56).en_US
dc.description.abstractBreakdown prediction of equipment is an essential task considering the management of resources and maintenance operations. Early diagnosis systems allow creating alerts on time for taking precautions on production. A significant challenge for diagnosis is to have an insufficient size of data, yet, transfer learning approaches can alleviate such an issue when there is a constrained supply of training data. We intend to improve the reliability of breakdown prediction when there is a limited quantity of training data. We recommend similarity correlation on Remaining Useful Life of these equipment. To do this, we offer learning a common feature space between the target and the source equipment, where we acquire prior knowledge from the source that has different measurements than the target. Within the learned joint feature matrices, we train our model on the vast amount of data of different equipment and finetune it using the data of our target equipment. In this way, we aim to obtain an accurate and reliable model for early breakdown prediction.en_US
dc.description.statementofresponsibilityby Seren Özbeken_US
dc.format.extentxii, 56 leaves : charts ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTransfer learningen_US
dc.subjectPredictive maintenanceen_US
dc.subjectFault diagnosisen_US
dc.subjectDeep learningen_US
dc.subjectLSTMen_US
dc.subjectCanonical correlation analysisen_US
dc.titleEarly diagnosis of breakdown through transfer learningen_US
dc.title.alternativeTransfer öğrenimi ile kestirimci bakımen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB132805
dc.embargo.release2019-12-14


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