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Titre: | Predicting Remaining Useful Life of Engines Using SVR and CNN |
Auteur(s): | Bouakel, Denis Redouane Mahmoudi, Hicham Namane, Rachid (Supervisor) |
Mots-clés: | Support Vector Machine (SVM) Convolutional Neural Network (CNN) |
Date de publication: | 2020 |
Résumé: | Engines’ Remaining Useful Life (RUL) prediction is a considerable issue to
realize Prognostics and Health Management (PHM) that is being widely applied in many
industrial systems to ensure high system availability over their life cycles. This work
presents a data-driven method of RUL prediction based on two Machine Learning (ML)
techniques, mainly Support Vector Machine (SVM) for Regression or Support Vector
Regression (SVR) and Convolutional Neural Network (CNN). These techniques are
applied on the NASA C-MAPSS turbofan engine dataset. To extract the input features,
the dataset was analyzed with the help of plots and a filter-based feature selection
technique known as Mutual Information (MI). the resulting features are then fed to both
models. Although SVM and CNN algorithms are mostly used in classification problems,
their effectiveness in estimating the RUL, which is a regression problem, is demonstrated
and compared to some state-of-the-art methods. The results show that the SVR and CNN
models provide approximately similar performance in predicting the RUL for the used
dataset. |
Description: | 43p. |
URI/URL: | http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/11790 |
Collection(s) : | Computer
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