• DocumentCode
    674345
  • Title

    Multiple fault detection technique for identifying broken rotor bars

  • Author

    Wangngon, B. ; Ruangsinchaiwanich, S.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Naresuan Univ., Phitsanuloke, Thailand
  • fYear
    2013
  • fDate
    26-29 Oct. 2013
  • Firstpage
    752
  • Lastpage
    756
  • Abstract
    This paper presents multiple fault detection technique for identifying the broken rotor bar condition based on the motor current signature method together with the artificial neural network. The artificial neural network has emerged potentially as an assistant tool for detecting the fault signal of the electrical machine because it is capable of recognizing patterns. Consequently, the proposed multiple fault detection techniques perform acceptably for recognizing the broken rotor bar problem of the induction motor.
  • Keywords
    bars; fault diagnosis; induction motors; neural nets; power engineering computing; rotors; signal detection; artificial neural network; broken rotor bars; electrical machine; fault signal detection; induction motor; motor current signature method; multiple fault detection technique; Agriculture; Induction motors; Prototypes; Rotors; Universal Serial Bus; Artificial Neural Network; Broken Rotor Bar; Current Signature Method; Fault Detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical Machines and Systems (ICEMS), 2013 International Conference on
  • Conference_Location
    Busan
  • Print_ISBN
    978-1-4799-1446-3
  • Type

    conf

  • DOI
    10.1109/ICEMS.2013.6713156
  • Filename
    6713156