• DocumentCode
    1939004
  • Title

    A New Method to Mechanical Fault Classification with Support Vector Machine

  • Author

    Sun, Laijun ; Liu, Mingliang ; Qian, Haibo ; Qiao, Changming

  • Author_Institution
    Coll. of Autom., Harbin Eng. Univ., Harbin, China
  • fYear
    2011
  • fDate
    5-7 Aug. 2011
  • Firstpage
    68
  • Lastpage
    73
  • Abstract
    In this paper, the basic principle of support vector machine is introduced firstly, Then a new method to diagnosis fault for high voltage cirCuit breakers is presented based on the introduction of wavelet packet and characteristic entropy. The new method decomposes vibration signals with wavelet packet, and extracts entropy parameters from the restructured signals at the third level. Finally, the new method and SVM are appLied to the fault recognition of cirCuit breakers, and the usable process is introduced in detail in the paper. In addition, SVM is compared with the artificial neural network, and the paper concludes that in terms of classification and learning speed, SVM is better than neural network clearly, and SVM is more appLicable to fault recognition of cirCuit breakers.
  • Keywords
    circuit breakers; condition monitoring; entropy; fault diagnosis; feature extraction; mechanical engineering computing; neural nets; pattern classification; support vector machines; vibrations; wavelet transforms; SVM; artificial neural network; entropy method; fault diagnosis; fault recognition; high voltage circuit breakers; mechanical fault classification; parameter extraction; support vector machine; vibration signal; wavelet packets; Circuit breakers; Circuit faults; Entropy; Support vector machines; Training; Vibrations; Wavelet packets; HV cirCuit breaker; Support Vector Machine; fault diagnosis; neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Digital Manufacturing and Automation (ICDMA), 2011 Second International Conference on
  • Conference_Location
    Zhangjiajie, Hunan
  • Print_ISBN
    978-1-4577-0755-1
  • Electronic_ISBN
    978-0-7695-4455-7
  • Type

    conf

  • DOI
    10.1109/ICDMA.2011.25
  • Filename
    6051854