• DocumentCode
    1731256
  • Title

    Intelligent condition monitoring and fault diagnosis of a gearbox based on Artificial Neural Network

  • Author

    Shulian, Yang ; Wenhai, Li ; Hua, Zhen ; Fang, Xiang

  • Author_Institution
    ShanDong Inst. of Bus. & Technol., Yantai
  • fYear
    2007
  • Abstract
    In this paper the vibration test system for the gearbox of mining machine , the wavelet denoising method , the artificial neural network´ s 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 with the excellent abilities of parallel distributed processing, self-study, self-adaptation, self-organization,associative memory and its highly non-linear pattern recognition is an efficient and feasible tool to solve complicated state identification problems in the gearbox fault diagnosis simultaneously.
  • Keywords
    backpropagation; condition monitoring; fault diagnosis; gears; neural nets; BP network structures; artificial neural network; associative memory; fault diagnosis; gearbox; intelligent condition monitoring; mining machine; nonlinear pattern recognition; parallel distributed processing; vibration test system; wavelet denoising; Artificial intelligence; Artificial neural networks; Condition monitoring; Distributed processing; Fault diagnosis; Intelligent networks; Machine intelligence; Neural networks; Noise reduction; System testing; Artifical Neural Network(ANN); Back Propagation(BP) Algorithm; Fault diagnosis; Gearbox; wavelet analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronic Measurement and Instruments, 2007. ICEMI '07. 8th International Conference on
  • Conference_Location
    Xi´an
  • Print_ISBN
    978-1-4244-1136-8
  • Electronic_ISBN
    978-1-4244-1136-8
  • Type

    conf

  • DOI
    10.1109/ICEMI.2007.4350980
  • Filename
    4350980