• DocumentCode
    684981
  • Title

    Fault diagnosis of hydraulic system based on improved BP neural network technology

  • Author

    Zhang Yinshuo ; Xia Jun ; Li Lei

  • Author_Institution
    No.3 Dept., Nanjing Artillery Acad., Langfang, China
  • Volume
    01
  • fYear
    2013
  • fDate
    16-18 Aug. 2013
  • Firstpage
    137
  • Lastpage
    140
  • Abstract
    A fault diagnosis model with BP network for a certain hydraulic system were described. The realization process of the fault diagnosis based on the improved BP algorithm was discussed. According to the experiment, the improved BP network has better learning ability, higher convergence rate ability and higher stability of learning and memory. The diagnosis results indicate that the presented diagnosis method has higher reliability and can attain the expected results, which can be applied to fault diagnosis of hydraulic system.
  • Keywords
    backpropagation; fault diagnosis; hydraulic systems; mechanical engineering computing; neural nets; BP neural network technology; convergence rate ability; fault diagnosis; hydraulic system; learning ability; learning stability; memory stability; Artificial intelligence; Biological neural networks; Fault diagnosis; Hydraulic systems; Mathematical model; Neurons; BP algorithm; fault diagnosis; hydraulic system; neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Measurement, Information and Control (ICMIC), 2013 International Conference on
  • Conference_Location
    Harbin
  • Print_ISBN
    978-1-4799-1390-9
  • Type

    conf

  • DOI
    10.1109/MIC.2013.6757933
  • Filename
    6757933