Broken Rotor Bars Fault Detection Based on Envelope Analysis Spectrum and Neural Network in Induction Motors

Authors

  • Saddam BENSAOUCHA Laboratoire d’Etude et Développement des Matériaux Semi-Conducteurs et D’électriques (LeDMaSD),Université Amar Telidji de Laghouat, Algerié
  • Sid Ahmed BESSEDIK Laboratoire d’Analyse et de Commande des Systèmes d’Energie et Réseaux Electriques (LACoSERE), Université Amar Telidji de Laghouat, Algeria
  • Aissa AMEUR Laboratoire d’Analyse et de Commande des Systèmes d’Energie et Réseaux Electriques (LACoSERE), Université Amar Telidji de Laghouat, Algeria
  • Abdellatif SEGHIOUR Laboratoire d’Etude et Développement des Matériaux Semi-Conducteurs et D’électriques (LeDMaSD),Université Amar Telidji de Laghouat, Algeria

DOI:

https://doi.org/10.51485/ajss.v3i3.66

Keywords:

Induction motors, Broken rotor bars, Faults detection, Envelope analysis, Neural networks

Abstract

In this paper, a study has presented the performance of a neural networks technique to detect the broken rotor bars (BRBs) fault in induction motors (IMs). In this context, the fast Fourier transform (FFT) applied on Hilbert modulus obtained via the stator current signal has been used as a diagnostic signal to replace the FFT classic, the characteristics frequency are selected from the Hilbert modulus spectrum, in addition, the different load conditions are used as three inputs data for the neural networks. The efficiency of the proposed method is verified by simulation in MATLAB environment.

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Published

2018-09-15

How to Cite

[1]
BENSAOUCHA, S. , BESSEDIK, S.A. , AMEUR, A. and SEGHIOUR, A. 2018. Broken Rotor Bars Fault Detection Based on Envelope Analysis Spectrum and Neural Network in Induction Motors. Algerian Journal of Signals and Systems . 3, 3 (Sep. 2018), 106-116. DOI:https://doi.org/10.51485/ajss.v3i3.66.

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Section

Articles