• DocumentCode
    2795749
  • Title

    Algorithm of Data Mining and its Application in Fault Diagnosis for Wind Turbine

  • Author

    Chen, Zhigang ; Lian, Xiangjiao ; Yu, Huiyuan ; Bao, Zhongli

  • Author_Institution
    Dept. of Mechanic Eng., Beijing Univ. of Civil Eng. & Archit., Beijing, China
  • Volume
    2
  • fYear
    2009
  • fDate
    Nov. 30 2009-Dec. 1 2009
  • Firstpage
    240
  • Lastpage
    243
  • Abstract
    Because more and more characteristic parameters are used to describe machine vibration, few and more parameters selected will usually affect the right ration of diagnosis. In this paper an algorithm of data mining based on fuzzy clustering is approached, which is used to mine the optimal characteristic parameters from the parameters describing the vibration wave of gear cases of wind turbines. The detailed steps of data mining method were introduced. The characteristic parameters mined were used as optimal feature eigenvectors for diagnosing faults of gear cases. Applied example was shown that the result of fault diagnosis has been proved to be reliable and accurate.
  • Keywords
    data mining; fault diagnosis; fuzzy set theory; gears; wind turbines; data mining; fault diagnosis; fuzzy clustering; gear cases; machine vibration; optimal feature eigenvectors; wind turbine; Data analysis; Data engineering; Data mining; Databases; Fault diagnosis; Gears; Knowledge acquisition; Pattern recognition; Sensor phenomena and characterization; Wind turbines; data mining; fault diagnosis; gear case; optimal Feature; wind Turbine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3888-4
  • Type

    conf

  • DOI
    10.1109/KAM.2009.52
  • Filename
    5362076