A Deep Learning-Based Stacking Ensemble Framework for Turbofan Engine Remaining Useful Life Prediction
A two-level stacking ensemble framework for Remaining Useful Life prediction of turbofan engines, evaluated on the NASA C-MAPSS benchmark using the FD001 and FD003 subsets, demonstrates the efficacy of stacking ensemble methods for prognostics and health management in safety-critical aerospace applications.
Limon Bin Hossain, Md. Salehin Seyam, Md. Rashedul Islam et al.
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