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| Titre: | Remaining Useful Life Prediction in Turbofan Engines: PCA and Machine Learing Approach |
| Auteur(s): | Boutora, Saliha
Aribi, Yacine
Boushaki Zamoum, Pr. Razika
Kouzou, Pr. Abdellah
Menasria, Hafid
Abdellaoui, Abdelkader |
| Mots-clés: | Failure Prediction
Gradient Boosted Machine
Principal Component Analysis (PCA)
Random Forest
Remaining Useful Life (RUL)
Turbo Fan Engine |
| Date de publication: | 2023 |
| Editeur: | Institute of Electrical and Electronics Engineers Inc |
| Collection/Numéro: | 2023 20th International Multi-Conference on Systems, Signals & Devices (SSD), Mahdia, Tunisia, 2023;pp. 469-476 |
| Résumé: | 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 |
| URI/URL: | https://ieeexplore.ieee.org/document/10411236
10.1109/SSD58187.2023.10411236
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/13650 |
| ISSN: | 979-835033256-8 |
| Collection(s) : | Publications Internationales
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