• DocumentCode
    3253483
  • Title

    The intelligent fault modeling of induction motor

  • Author

    Ling-yan, Lin ; Jian-cheng, Song ; Mu-qin, Tian

  • Author_Institution
    Coll. of Electr. & Power Eng., Taiyuan Univ. of Technol., Taiyuan, China
  • fYear
    2010
  • fDate
    14-17 June 2010
  • Firstpage
    218
  • Lastpage
    220
  • Abstract
    The study of early intelligent obstacle diagnosis for large-sized mechanical equipment is of momentous social significance and far-reaching economic importance. The equipment directly influences production safety for enterprises, and concern economic efficiencies. So it is very important for the induction motor to guarantee non-failure work time and the whole cutting process without fault. The early-term fault and in-time diagnosis, however, is the most basic prerequisite for the amount ahead of schedule of maintenance.
  • Keywords
    fault diagnosis; induction motors; maintenance engineering; fault diagnosis; induction motor; intelligent fault modeling; maintenance scheduling; nonfailure work time; Background noise; Fault diagnosis; Frequency; Hopfield neural networks; Induction motors; Neural networks; Power generation economics; Signal processing; Vibrations; Wavelet packets; fault modelling; inteligence; neuralnetwork;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Probabilistic Methods Applied to Power Systems (PMAPS), 2010 IEEE 11th International Conference on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4244-5720-5
  • Type

    conf

  • DOI
    10.1109/PMAPS.2010.5529002
  • Filename
    5529002