• DocumentCode
    2564726
  • Title

    Vibration signature analysis for detecting cavitation in centrifugal pumps using neural networks

  • Author

    Nasiri, M.R. ; Mahjoob, M.J. ; Vahid-Alizadeh, H.

  • Author_Institution
    NVA Res. Center, Univ. of Tehran, Tehran, Iran
  • fYear
    2011
  • fDate
    13-15 April 2011
  • Firstpage
    632
  • Lastpage
    635
  • Abstract
    Vibration analysis is applied to detect cavitation in a centrifugal pump using a neural net system. The features extracted from vibration signals are used as inputs to the neural network. The output data of the system is set as 0,0.5 and 1, for normal condition, developed cavitation and fully developed cavitation, respectively. Experiments are also conducted to validate the developed model. The method provides an intelligent system to be used in condition monitoring of centrifugal pumps. Also the number of sensors and the best sensor positions are studied.
  • Keywords
    cavitation; condition monitoring; feature extraction; mechanical engineering computing; neural nets; pumps; signal processing; vibrations; cavitation detection; centrifugal pumps; feature extraction; neural networks; sensor positions; vibration signals; vibration signature analysis; Irrigation; Noise; Noise measurement; cavitation; centrifugal pump; neural network; vibration signal processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechatronics (ICM), 2011 IEEE International Conference on
  • Conference_Location
    Istanbul
  • Print_ISBN
    978-1-61284-982-9
  • Type

    conf

  • DOI
    10.1109/ICMECH.2011.5971192
  • Filename
    5971192