Stacking-based ensemble learning for remaining useful life estimation

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dc.authorid0000-0003-0959-2930en_US
dc.contributor.authorTure, Begum Ay
dc.contributor.authorAkbulut, Akhan
dc.contributor.authorZaim, Abdul Halim
dc.contributor.authorCatal, Cagatay
dc.date.accessioned2023-11-08T07:26:36Z
dc.date.available2023-11-08T07:26:36Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractExcessive and untimely maintenance prompts economic losses and unnecessary workload. Therefore, predictive maintenance models are developed to estimate the right time for maintenance. In this study, predictive models that estimate the remaining useful life of turbofan engines have been developed using deep learning algorithms on NASA’s turbofan engine degradation simulation dataset. Before equipment failure, the proposed model presents an estimated timeline for maintenance. The experimental studies demonstrated that the stacking ensemble learning and the convolutional neural network (CNN) methods are superior to the other investigated methods. While the convolution neural network (CNN) method was superior to the other investigated methods with an accuracy of 93.93%, the stacking ensemble learning method provided the best result with an accuracy of 95.72%.en_US
dc.identifier.doi10.1007/s00500-023-08322-6en_US
dc.identifier.scopus2-s2.0-85160273776en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6936
dc.identifier.urihttps://doi.org/10.1007/s00500-023-08322-6
dc.identifier.wosWOS:000991632000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofSoft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRemaining useful life, Ensemble learning, Deep learning, Stacking ensemble learningen_US
dc.titleStacking-based ensemble learning for remaining useful life estimationen_US
dc.typeArticleen_US

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