• DocumentCode
    966233
  • Title

    Asynchronous Machine Rotor Fault Diagnosis Technique Using Complex Wavelets

  • Author

    Tsoumas, Ioannis P. ; Georgoulas, George ; Mitronikas, Epaminondas D. ; Safacas, Athanasios N.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Patras, Rio-Patras
  • Volume
    23
  • Issue
    2
  • fYear
    2008
  • fDate
    6/1/2008 12:00:00 AM
  • Firstpage
    444
  • Lastpage
    459
  • Abstract
    This paper introduces a novel approach for the detection of rotor faults in asynchronous machines, based on wavelet analysis of the stator phase current. To be more specific, the measured stator phase current is filtered through a complex wavelet. Theoretical analysis validates that the spectrum of the modulus of the result of the filtering is free from the fundamental supply frequency component, and the fault characteristics can be highlighted. This is advantageous, especially if the induction machine operates at low slip values, where the characteristic frequency components of the rotor fault are very close to the fundamental frequency component. At the same time, by matching the wavelet function to the frequencies of the faulty components, a narrow bandpass filter at the frequency region of the fault characteristic spectral components is obtained. Furthermore, in the context of this paper, features extracted using the proposed technique are used as input to a support vector machine classifier that is employed for the detection of the rotor fault. Simulation and experimental results demonstrate the effectiveness of the proposed technique.
  • Keywords
    asynchronous machines; fault diagnosis; feature extraction; rotors; spectral analysis; support vector machines; wavelet transforms; asynchronous machine; complex wavelet; fault characteristic spectral components; features extraction; narrow bandpass filter; rotor fault detection; rotor fault diagnosis; stator phase current; support vector machine classifier; wavelet analysis; Asynchronous machines; fault diagnosis; feature extraction; monitoring; pattern recognition; wavelet transforms;
  • fLanguage
    English
  • Journal_Title
    Energy Conversion, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8969
  • Type

    jour

  • DOI
    10.1109/TEC.2007.895872
  • Filename
    4378206