Stacking-based ensemble learning for remaining useful life estimation

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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%.

Original languageEnglish
Pages (from-to)1337-1349
Number of pages13
JournalSoft Computing
Issue number2
Publication statusPublished - Jan 2024
Externally publishedYes

Bibliographical note

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


  • Deep learning
  • Ensemble learning
  • Remaining useful life
  • Stacking ensemble learning


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