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Veuillez utiliser cette adresse pour citer ce document : http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/15433

Titre: Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques
Auteur(s): Mahami, Amine
Rahmoune, Chemseddine
Bettahar, Toufik
Benazzouz, Djamel
Mots-clés: Infrared thermography images
Induction motor
Faults diagnosis
Feature extraction
Extremely randomized tree
Date de publication: 2021
Editeur: SAGE
Collection/Numéro: Advances in Mechanical Engineering / Vol.13, N°11;pp. 1–13
Résumé: In this paper, a novel noncontact and nonintrusive framework experimental method is used for the monitoring and the diagnosis of a three phase’s induction motor faults based on an infrared thermography technique (IRT). The basic structure of this work begins with this applying IRT to obtain a thermograph of the considered machine. Then, bag-of-visual-word (BoVW) is used to extract the fault features with Speeded-Up Robust Features (SURF) detector and descriptor from the IRT images. Finally, various faults patterns in the induction motor are automatically identified using an ensemble learning called Extremely Randomized Tree (ERT). The proposed method effectiveness is evaluated based on the experimental IRT images, and the diagnosis results show its capacity and that it can be considered as a powerful diagnostic tool with a high classification accuracy and stability compared to other previously used methods.
URI/URL: https://doi.org/10.1177/16878140211060956
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/15433
Collection(s) :Publications Internationales

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