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Communication Dans Un Congrès Année : 2022

Contrast Feature-Based Approach for Fault Detection in Wound-Rotor Induction Machines

Résumé

Fault detection in induction machines has become a topic of great interest for researchers due to the growing utility these are giving to today’s industry. Real time machine learning methods are recently proposed in this area to improve the precision of fault detection. In this paper, a novel methodology based on texture feature estimation using an artificial neural network is proposed to be used for fault detection of induction machines. The method is tested for a typical fault as rotor phase opening of a wound rotor induction machine. For this, the stator current in time domain is used to detect the fault based on the proposed method. The experimental results are shown for several loads and speeds conditions to prove the effectiveness of the method.
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Dates et versions

hal-04011793 , version 1 (02-03-2023)

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Citer

Edna Ferrucho-Alvarez, Mehdi Taherzadeh, Humberto Henao, Gerard-Andre Capolino, Eduardo Cabal-Yepez. Contrast Feature-Based Approach for Fault Detection in Wound-Rotor Induction Machines. 2022 International Conference on Electrical Machines (ICEM), Sep 2022, Valencia, Spain. pp.670-676, ⟨10.1109/ICEM51905.2022.9910754⟩. ⟨hal-04011793⟩

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