• DocumentCode
    1902199
  • Title

    Diagnosis of stator winding inter-turn short circuit in three-phase induction motors by using artificial neural networks

  • Author

    Broniera, P.J. ; Gongora, W.S. ; Goedtel, A. ; Godoy, W.F.

  • Author_Institution
    Dept. of Electr. Eng., UTFPR Univ., Cornelio Procopio, Brazil
  • fYear
    2013
  • fDate
    27-30 Aug. 2013
  • Firstpage
    281
  • Lastpage
    287
  • Abstract
    The application of induction motors in industry is widespread. Thus, several studies have presented strategies for the diagnosis and prediction of failures in these motors. One technique used is based on the recent utilization of intelligent systems for detecting faults in electric motors. Thus, this paper proposes an alternative tool to traditional techniques for fault detection of a short circuit between the inter-turns of the stator winding using artificial neural networks to analyze stator current signals in the time domain. Experimental results are presented to validate the proposed approach.
  • Keywords
    fault diagnosis; induction motors; neural nets; power engineering computing; stators; time-domain analysis; artificial neural networks; fault detection; stator current signals; stator winding interturn short circuit diagnosis; three-phase induction motors; time domain analysis; Artificial neural networks; Circuit faults; Induction motors; Neurons; Stator windings; Training; Artificial Neural Networks; Stator faults; Three phase induction motors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), 2013 9th IEEE International Symposium on
  • Conference_Location
    Valencia
  • Type

    conf

  • DOI
    10.1109/DEMPED.2013.6645729
  • Filename
    6645729