• DocumentCode
    2317236
  • Title

    The intelligent fault diagnosis of wind turbine gearbox based on artificial neural network

  • Author

    Yang, Shulian ; Li, Wenhai ; Wang, Canlin

  • Author_Institution
    Comput. Dept., ShanDong Inst. of Bus. & Technol., Yantai
  • fYear
    2008
  • fDate
    21-24 April 2008
  • Firstpage
    1327
  • Lastpage
    1330
  • Abstract
    The vibration test system for the gearbox of wind turbine , the wavelet denoising method , the artificial neural networkpsilas essential principles and its features, BP network structures model in the gearbox fault diagnosis are discussed. Tested vibration signals are disposed by the method of wavelet denoising and than as the inputs of BP neural network. By using classical BP neural network, four kinds of typical patterns of gearbox faults have been studied and diagnosed ,and satisfied results have been acquired. The research results indicate that BP neural network have the excellent abilities of parallel distributed processing, self-study, self-adaptation, self-organization, associative memory , and simultaneously its highly non-linear pattern recognition technology is an efficient and feasible tool to solve complicated state identification problems in the gearbox fault diagnosis.
  • Keywords
    backpropagation; fault diagnosis; neural nets; pattern recognition; switchgear; wind turbines; artificial neural network; backpropagation neural network; intelligent fault diagnosis; nonlinear pattern recognition; parallel distributed processing; vibration test system; wavelet denoising; wind turbine gearbox; Artificial intelligence; Artificial neural networks; Associative memory; Distributed processing; Fault diagnosis; Intelligent networks; Neural networks; Noise reduction; System testing; Wind turbines; Artificial Neural Network(ANN); Back Propagation( BP); Denoising; Fault diagnosis; Gearbox; Vibration; wavelet analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Condition Monitoring and Diagnosis, 2008. CMD 2008. International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-1621-9
  • Electronic_ISBN
    978-1-4244-1622-6
  • Type

    conf

  • DOI
    10.1109/CMD.2008.4580221
  • Filename
    4580221