Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8226
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dc.contributor.authorDeveci, B.U.-
dc.contributor.authorÇeltikoğlu, M.-
dc.contributor.authorAlp, T.-
dc.contributor.authorAlbayrak, O.-
dc.contributor.authorÜnal, P.-
dc.contributor.authorKırcı, P.-
dc.date.accessioned2022-01-15T13:00:40Z-
dc.date.available2022-01-15T13:00:40Z-
dc.date.issued2021-
dc.identifier.isbn9781665425742-
dc.identifier.urihttps://doi.org/10.1109/FiCloud49777.2021.00048-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8226-
dc.description8th International Conference on Future Internet of Things and Cloud, FiCloud 2021 -- 23 August 2021 through 25 August 2021 -- 173916en_US
dc.description.abstractIn rotating machinery, bearings are widely used as universal components. Bearings are placed in critical positions, therefore, in predictive maintenance, it is crucial to diagnose bearing faults accurately and in a timely manner. In this paper, three diverse pre-trained networks on bearing fault diagnosis are discussed. A generic intelligent bearing fault diagnosis system based on AlexNet, GoogLeNet and ResNet-50 with transfer learning is proposed to distinguish and classify different bearing faults. Three bearing faults at all various loads and speeds selected from the Case Western Reserve University (CWRU) bearing dataset were converted to time-frequency images, in order to improve the performance of the proposed networks. Results showed that when compared to previous methods, the proposed method achieved outstanding execution, with overall classification training accuracy of 100%, validation accuracy of 99.27%. © 2021 IEEE.en_US
dc.description.sponsorship870130; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAKen_US
dc.description.sponsorshipThis study was conducted by TEKNOPAR and supported partially by the COGNITWIN (Cognitive Plants Through Proactive Self-Learning Hybrid Digital Twins) project and by TUBITAK (The Scientific and Technological Research Council of Turkey). The COGNITWIN was funded by the European Union ?s Horizon 2020 research and innovation programme under GA No.870130.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2021 International Conference on Future Internet of Things and Cloud, FiCloud 2021en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAlexNeten_US
dc.subjectbearing fault diagnosticsen_US
dc.subjectCNNen_US
dc.subjectCWRU bearing dataseten_US
dc.subjectdeep learningen_US
dc.subjectGoogLeNeten_US
dc.subjectResNet-50en_US
dc.subjectTransfer learningen_US
dc.subjectBearings (machine parts)en_US
dc.subjectFailure analysisen_US
dc.subjectFault detectionen_US
dc.subjectImage enhancementen_US
dc.subjectAlexneten_US
dc.subjectBearing faulten_US
dc.subjectBearing fault diagnosticsen_US
dc.subjectCase Western Reserve Universityen_US
dc.subjectCase western reserve university bearing dataseten_US
dc.subjectCNNen_US
dc.subjectDeep learningen_US
dc.subjectGoogleneten_US
dc.subjectResnet-50en_US
dc.subjectTransfer learningen_US
dc.subjectDeep learningen_US
dc.titleA Comparison of Deep Transfer Learning Methods on Bearing Fault Detectionen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.identifier.startpage285en_US
dc.identifier.endpage292en_US
dc.identifier.scopus2-s2.0-85119661402en_US
dc.institutionauthorDeveci, Bilgin Umut-
dc.identifier.doi10.1109/FiCloud49777.2021.00048-
dc.authorscopusid57350944900-
dc.authorscopusid57205335558-
dc.authorscopusid57226393865-
dc.authorscopusid57226393431-
dc.authorscopusid56396952700-
dc.authorscopusid15026635000-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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