Türe, Begüm AyAKBULUT, AKHANZaim, Abdul HalimÇatal, Çağatay2023-10-122023-10-122023Ture, B.A., Akbulut, A., Zaim, A.H. et al. Stacking-based ensemble learning for remaining useful life estimation. Soft Comput (2023).1432-7643https://doi.org/10.1007/s00500-023-08322-6https://hdl.handle.net/11413/8823Excessive 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%.eninfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivs 3.0 United Stateshttp://creativecommons.org/licenses/by-nc-nd/3.0/us/Remaining Useful LifeEnsemble LearningDeep LearningStacking Ensemble LearningArticle Early Access0009916320000012-s2.0-85160273776