• DocumentCode
    1986095
  • Title

    Application of support vector machine nonlinear classifier to fault diagnoses

  • Author

    Yan, Weiwu ; Shao, Huihe

  • Author_Institution
    Dept. of Autom., Shanghai Jiao Tong Univ., China
  • Volume
    4
  • fYear
    2002
  • fDate
    2002
  • Firstpage
    2697
  • Abstract
    Support vector machine (SVM) is a novel machine learning method based on statistical learning theory. SVM is a powerful tool for solving problems with small samples, nonlinearities and local minima, and is of excellent performance in classification. In the paper, the SVM nonlinear classification algorithm is reviewed. The SVM nonlinear classifier is applied to deal with fault diagnosis. SVM is easy to implement for fault diagnosis. Effective results are obtained of using the SVM for fault diagnosis.
  • Keywords
    fault diagnosis; learning automata; neural nets; pattern classification; statistical analysis; fault diagnosis; machine learning; nonlinear classifier; pattern classification; statistical learning; support vector machine; Automation; Classification algorithms; Fault diagnosis; Kernel; Lagrangian functions; Learning systems; Signal processing algorithms; Statistical learning; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
  • Print_ISBN
    0-7803-7268-9
  • Type

    conf

  • DOI
    10.1109/WCICA.2002.1020004
  • Filename
    1020004