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Communication Dans Un Congrès Année : 2018

Induction Machine Fault Diagnosis Using Stator Current Subspace Spectral Estimation

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R. R. Kumar
  • Fonction : Auteur
M. Cirrincione
  • Fonction : Auteur
A. Tortella
  • Fonction : Auteur
M. Andriollo
  • Fonction : Auteur

Résumé

This paper presents a subspace-based approach to identify and extract harmonics of interest for the diagnosis of stator and rotor related faults in induction machines. The major goal of this paper is firstly to introduce and highlight the effectiveness of prominence measure upon preparing features for classification of faults. Secondly, a new approach is presented here to retrieve harmonics by using prominence measure of the peaks for each case of the fault. Finally, a hierarchical multi-layer perceptron neural network has been used as the classifier and compared with other existing classification algorithms to deduce the best model. The effectiveness of the developed scheme is verified experimentally.
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Dates et versions

hal-03631449 , version 1 (05-04-2022)

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  • HAL Id : hal-03631449 , version 1

Citer

R. R. Kumar, G. Cirrincione, M. Cirrincione, A. Tortella, M. Andriollo. Induction Machine Fault Diagnosis Using Stator Current Subspace Spectral Estimation. 2018 21ST INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES AND SYSTEMS (ICEMS), Oct 2018, Jeju, South Korea. pp.2565-2570. ⟨hal-03631449⟩

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