Publications Scientifiques

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Author: search.filters.author.Aribi, YacineSubject: search.filters.subject.Gradient Boosted Machine

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    Turbofan Engine RUL Prediction using ICA and Machine Learning Algorithms
    (Institute of Electrical and Electronics Engineers Inc, 2023) Aribi, Yacine; Boutora, Saliha; Boushaki Zamoum, Pr. Razika; Menasria, Hafid; Abdellaoui, Abdelkader; Kouzou, Pr. Abdellah
    This paper takes an approach to the determination on the Remaining Useful Life (RUL) on a real-life turbo engine model selected from a set of data provided within the public domain for research purposes from the Prognostics Data Repository of NASA. The RUL analysis algorithm uses Independent Component Analysis for data dimensionality reduction and data processing simplicity due to the large number of involved sensors, then a model is trained to predict the remaining useful life for the turbo engine using Random Forest (RF) and Gradient Boosted Machine Algorithms (GBMA). The final RUL data is compared to the real RUL vs. time provided within the original data date for algorithm validation.
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    Remaining Useful Life Prediction in Turbofan Engines: PCA and Machine Learing Approach
    (Institute of Electrical and Electronics Engineers Inc, 2023) Boutora, Saliha; Aribi, Yacine; Boushaki Zamoum, Pr. Razika; Kouzou, Pr. Abdellah; Menasria, Hafid; Abdellaoui, Abdelkader
    In 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 engine