Algerian Journal of Signals and Systems

Algerian Journal of Signals and Systems

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

Yazarlar: Saddam BENSAOUCHA, Sid Ahmed BESSEDIK, Aissa AMEUR, Abdellatif SEGHIOUR

Cilt 3 , Sayı 3 , 2018 , Sayfalar 106-116

Konular:-

DOI:10.51485/ajss.v3i3.66

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

Özet: 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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@article{2018, title={Broken Rotor Bars Fault Detection Based on Envelope Analysis Spectrum and Neural Network in Induction Motors}, volume={3}, number={106–116}, publisher={Algerian Journal of Signals and Systems}, author={Saddam BENSAOUCHA, Sid Ahmed BESSEDIK, Aissa AMEUR, Abdellatif SEGHIOUR}, year={2018} }
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Saddam BENSAOUCHA, Sid Ahmed BESSEDIK, Aissa AMEUR, Abdellatif SEGHIOUR. (2018). Broken Rotor Bars Fault Detection Based on Envelope Analysis Spectrum and Neural Network in Induction Motors (Vol. 3). Vol. 3. Algerian Journal of Signals and Systems.
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Saddam BENSAOUCHA, Sid Ahmed BESSEDIK, Aissa AMEUR, Abdellatif SEGHIOUR. Broken Rotor Bars Fault Detection Based on Envelope Analysis Spectrum and Neural Network in Induction Motors. no. 106–116, Algerian Journal of Signals and Systems, 2018.
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