Damage Detection on Turbomachinery with Machine Learning Algortihms

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dc.authorscopusid58784029100
dc.authorscopusid6602703521
dc.contributor.authorÖzçelik, Ahmet Devlet
dc.contributor.authorÖktem, Ahmet Sinan
dc.date.accessioned2024-09-11T19:58:01Z
dc.date.available2024-09-11T19:58:01Z
dc.date.issued2024
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description2nd International Conference on Advanced Engineering, Technology and Applications, ICAETA 2023 -- 10 March 2023 through 11 March 2023 -- Istanbul -- 305999en_US
dc.description.abstractThis study uses machine learning methods to find deterioration in turbomachine parts. In turbomachines, damage control procedures are carried out at specific times. Even though these checks take a while, if there is no damage, the components won’t be replaced, and it is not anticipated that they will be rechecked until the following control or an unforeseen incident. For this situation, a machine learning algorithm has been developed and 96% accuracy was obtained for overall components. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.identifier.doi10.1007/978-3-031-50920-9_19
dc.identifier.endpage253en_US
dc.identifier.isbn978-303150919-3en_US
dc.identifier.issn1865-0929en_US
dc.identifier.scopus2-s2.0-85180808150en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage242en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-50920-9_19
dc.identifier.urihttps://hdl.handle.net/11363/8397
dc.identifier.volume1983 CCISen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofCommunications in Computer and Information Scienceen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectMachine Learning; Predictive Maintenance; Turbo Machineryen_US
dc.titleDamage Detection on Turbomachinery with Machine Learning Algortihmsen_US
dc.typeConference Objecten_US

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