Stacking-based ensemble learning for remaining useful life estimation

Begum Ay Ture, Akhan Akbulut, Abdul Halim Zaim, Cagatay Catal*

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Araştırma sonucu: ???type-name???Makalebilirkişi

4 Atıf (Scopus)

Özet

Excessive 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%.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)1337-1349
Sayfa sayısı13
DergiSoft Computing
Hacim28
Basın numarası2
DOI'lar
Yayın durumuYayınlandı - Oca 2024
Harici olarak yayınlandıEvet

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Publisher Copyright:
© 2023, The Author(s).

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