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dc.contributor.authorSelçuk, Şahan Yoruç
dc.contributor.authorÜnal, Perin
dc.contributor.authorAlbayrak, Özlem
dc.contributor.authorJomâa, Moez
dc.date.accessioned2022-04-04T08:44:32Z
dc.date.available2022-04-04T08:44:32Z
dc.date.created2021-10-07T14:12:56Z
dc.date.issued2021
dc.identifier.citationInformation. 2021, 12 (10), .en_US
dc.identifier.issn2078-2489
dc.identifier.urihttps://hdl.handle.net/11250/2989451
dc.description.abstractDigital twins, virtual representations of real-life physical objects or processes, are becoming widely used in many different industrial sectors. One of the main uses of digital twins is predictive maintenance, and these technologies are being adapted to various new applications and datatypes in many industrial processes. The aim of this study was to propose a methodology to generate synthetic vibration data using a digital twin model and a predictive maintenance workflow, consisting of preprocessing, feature engineering, and classification model training, to classify faulty and healthy vibration data for state estimation. To assess the success of the proposed workflow, the mentioned steps were applied to a publicly available vibration dataset and the synthetic data from the digital twin, using five different state-of-the-art classification algorithms. For several of the classification algorithms, the accuracy result for the classification of healthy and faulty data achieved on the public dataset reached approximately 86%, and on the synthetic data, approximately 98%. These results showed the great potential for the proposed methodology, and future work in the area.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectvibration dataen_US
dc.subjectdigital twinen_US
dc.subjectpredictive maintenanceen_US
dc.titleA workflow for synthetic data generation and predictive maintenance for vibration dataen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).en_US
dc.source.pagenumber14en_US
dc.source.volume12en_US
dc.source.journalInformationen_US
dc.source.issue10en_US
dc.identifier.doi10.3390/info12100386
dc.identifier.cristin1944176
dc.source.articlenumber386en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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