• DocumentCode
    581361
  • Title

    On the use of stationary wavelet packet transform and multiclass wavelet SVM for broken rotor bar detection

  • Author

    Keskes, Hassen ; Braham, Ahmed ; Lachiri, Zied

  • Author_Institution
    Res. Lab., INSAT, Tunisia
  • fYear
    2012
  • fDate
    25-28 Oct. 2012
  • Firstpage
    3919
  • Lastpage
    3924
  • Abstract
    This paper proposes an original combination of Stationary Wavelet Packet Transform (SWPT) and Multiclass Wavelet Support Vector Machines (MWSVM) to detect broken rotor bar (BRB) in induction motor (IM). The SWPT is used for feature extraction under lower sampling rate. MWSVM is developed to perform the faults recognition. Different binary Multiclass SVM strategies are compared with various wavelet kernel functions in terms of classification accuracy, training and testing complexity. The experimental results show that the proposed method is able to detect the faulty conditions with high accuracy.
  • Keywords
    electric machine analysis computing; fault diagnosis; feature extraction; induction motors; rotors; support vector machines; wavelet transforms; IM; MWSVM; SWPT; binary multiclass SVM strategies; broken rotor bar detection; classification accuracy; faults recognition; feature extraction; induction motor; multiclass wavelet support vector machines; sampling rate; stationary wavelet packet transform; testing complexity; training; Accuracy; Amplitude modulation; Discrete wavelet transforms; Harmonic analysis; Fault detection; broken-rotorbar; induction motors; pattern recognition; support vector machines; wavelet transforms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society
  • Conference_Location
    Montreal, QC
  • ISSN
    1553-572X
  • Print_ISBN
    978-1-4673-2419-9
  • Electronic_ISBN
    1553-572X
  • Type

    conf

  • DOI
    10.1109/IECON.2012.6389266
  • Filename
    6389266