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This study presents an integrated approach for predicting the remaining useful life (RUL) of aircraft engines using advanced feature engineering, dimensionality reduction, and machine learning models. A novel aggregated feature importances with cross-validation (AFICv) technique is introduced to enhance model interpretability and performance, achieving competitive results with fewer features. The methodology is validated using the C-MAPSS dataset, demonstrating the effectiveness of the proposed techniques i

Author

rullyrchmt

Language

EN