Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

Accurate Fault Diagnosis and Classification Scheme based on Non-Parametric, Statistical-Frequency Features and Neural Networks

Abstract : This paper presents a fault diagnosis and classification scheme for induction machines by using motor current signature analysis together with neural networks. The adopted strategy utilizes three-phase stator current sensors and calculates appropriate features using non-parametric and a statistical approach. The feature-set is reduced by means of the principal component analysis which acts as a pre-processor for the multilayer perceptron neural network. This two stage classification is carried out for detection and classification of faults. The efficacy of the proposed scheme is validated experimentally by using grid and inverter fed induction motors.
Type de document :
Communication dans un congrès
Liste complète des métadonnées

https://hal-u-picardie.archives-ouvertes.fr/hal-03631450
Contributeur : Louise DESSAIVRE Connectez-vous pour contacter le contributeur
Soumis le : mardi 5 avril 2022 - 16:25:20
Dernière modification le : vendredi 5 août 2022 - 11:21:50

Identifiants

  • HAL Id : hal-03631450, version 1

Collections

Citation

R. R. Kumar, G. Cirrincione, M. Cirrincione, M. Andriollo, A. Tortella. Accurate Fault Diagnosis and Classification Scheme based on Non-Parametric, Statistical-Frequency Features and Neural Networks. 2018 XIII INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES (ICEM), Sep 2018, Alexandroupoli, Greece. pp.1747-1753. ⟨hal-03631450⟩

Partager

Métriques

Consultations de la notice

3