Boutora, SalihaAribi, YacineBoushaki Zamoum, Pr. RazikaKouzou, Pr. AbdellahMenasria, HafidAbdellaoui, Abdelkader2024-03-052024-03-052023979-835033256-8https://ieeexplore.ieee.org/document/1041123610.1109/SSD58187.2023.10411236https://dspace.univ-boumerdes.dz/handle/123456789/13650In this paper, a study of prediction of the Remaining Useful Life (RUL) of an aircraft's turbofan engine is explored by analyzing a data set of a real-life turbofan engine from the Prognostics Data Repository of NASA and using the Principal Component Analysis (PCA) for data reduction and preprocessing, then selecting machine learning algorithms, mainly the Random Forest (RF) and Gradient Boosted Machine (GBM) so that a model can be trained to predict the possible failures through developing a model to estimate the RUL of the functionality of the turbofan engineenFailure PredictionGradient Boosted MachinePrincipal Component Analysis (PCA)Random ForestRemaining Useful Life (RUL)Turbo Fan EngineArticle