• DocumentCode
    1894464
  • Title

    Application of radial basis neural networks for the rotor fault detection of the induction motor

  • Author

    Kaminski, Marcin ; Kowalski, Czeslaw T. ; Orlowska-Kowalska, Teresa

  • Author_Institution
    Fac. of Electr. Eng., Wroclaw Univ. of Technol., Wroclaw, Poland
  • fYear
    2011
  • fDate
    27-29 April 2011
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The main stages of the design methodology of the radial basis neural detectors are described. Furthermore, influence of neural networks complexity and parameters of RBF activation function on quality of data classification is shown. Presented neural detectors are tested with data obtained in laboratory setup contained of converter-fed induction motor and changeable rotors with different degree of damages.
  • Keywords
    electric machine analysis computing; fault diagnosis; induction motors; radial basis function networks; rotors; RBF activation function; data classification; induction motor; neural networks complexity; radial basis neural networks; rotor fault detection; Artificial neural networks; Bars; Fault detection; Induction motors; Neurons; Rotors; Stators; RBF neural networks; diagnostic symptoms; fault detector; induction motor; rotor fault;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    EUROCON - International Conference on Computer as a Tool (EUROCON), 2011 IEEE
  • Conference_Location
    Lisbon
  • Print_ISBN
    978-1-4244-7486-8
  • Type

    conf

  • DOI
    10.1109/EUROCON.2011.5929405
  • Filename
    5929405
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1894464