• DocumentCode
    533584
  • Title

    Fault diagnosis of air-conditioning fan based on RBF neural networks algorithm

  • Author

    Yi, Wang

  • Author_Institution
    Dept. of Building Environ. & Equip. Eng., Donghua Univ., Shanghai, China
  • Volume
    1
  • fYear
    2010
  • fDate
    1-2 Aug. 2010
  • Firstpage
    304
  • Lastpage
    306
  • Abstract
    In order to overcome the problems of slow rate of convergence, falling easily into local minimum and instability of learning performance caused by initial value in BP algorithm, the diagnosis method based on RBF neural networks was proposed. And the diagnosis method is applied to air-conditioning fan fault diagnosis. The result shows that RBF network has very high learning convergence speed and better classifying performance. RBF network has good practicality in the field of equipment fault diagnosis.
  • Keywords
    air conditioning; backpropagation; fans; fault diagnosis; learning (artificial intelligence); mechanical engineering computing; quality assurance; radial basis function networks; RBF neural networks algorithm; air conditioning fan fault diagnosis; backpropagation algorithm; diagnosis method; equipment fault diagnosis; high learning convergence speed; Classification algorithms; Convergence; Cryptography; Air-conditioning fan; Fault Diagnosis; RBF neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits,Communications and System (PACCS), 2010 Second Pacific-Asia Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-7969-6
  • Type

    conf

  • DOI
    10.1109/PACCS.2010.5626978
  • Filename
    5626978