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Soft Computing
Erschienen in: Soft Computing 22/2017

16.06.2016 | Methodologies and Application

Bearing fault identification of three-phase induction motors bases on two current sensor strategy

verfasst von: Tiago Drummond Lopes, Alessandro Goedtel, Rodrigo Henrique Cunha Palácios, Wagner Fontes Godoy, Roberto Molina de Souza

Erschienen in: Soft Computing | Ausgabe 22/2017

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Abstract

Three-phase induction motors are the most commonly used devices for electromechanical energy conversion. This study proposes an alternative approach for identifying bearing faults in induction motors, using two current sensors and a pattern classifier, based on artificial neural networks. To validate the methodology, results are given from experiments carried out on a test bench where the motors operate with different types of bearing faults, under varying conditions of load torque and voltage unbalance. This paper also provides the comparative performance of neural network and random forest classifiers. This study also presents an analysis of the current signals in the time domain, applied to different neural structures.
Vorheriger Artikel A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms
Nächster Artikel Different distance measures for fuzzy linear regression with Monte Carlo methods

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Metadaten
Titel
Bearing fault identification of three-phase induction motors bases on two current sensor strategy
verfasst von
Tiago Drummond Lopes
Alessandro Goedtel
Rodrigo Henrique Cunha Palácios
Wagner Fontes Godoy
Roberto Molina de Souza
Publikationsdatum
16.06.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 22/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-016-2217-8

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