• DocumentCode
    2437262
  • Title

    Research on Information Fusion Fault Diagnosis System Based on Fuzzy Neural Network

  • Author

    Yang, Fan ; Liao, Zhi

  • Author_Institution
    Sch. of Electr. & Inf. Eng., Wuhan Inst. of Technol., Wuhan
  • Volume
    2
  • fYear
    2008
  • fDate
    19-20 Dec. 2008
  • Firstpage
    164
  • Lastpage
    167
  • Abstract
    For limitation of fault diagnosis methods in the engineering machine´s hydraulic system, a fault diagnosis method based on multi-sensor information fusion was presented. This method simultaneously had the faculty that fuzzy theory could process uncertain or inaccurate information and the self-study capability of the neural network, which effectively enhanced the fault diagnosis´s technical level collecting samples of data through establishing many sensors in the scene of the hydraulic system, and getting the membership of the fault through the membership function, then through the training of fuzzy neural network by the BP algorithm to achieve exact fault diagnosis function of hydraulic braking system. By contrast the diagnosis result of an example, it indicates that using multi-sensor information fusion as fault diagnosis method is more accurate and reliable than using single information as fault diagnosis method in the hydraulic system fault diagnosis.
  • Keywords
    backpropagation; braking; fault diagnosis; fuzzy neural nets; fuzzy set theory; hydraulic systems; mechanical engineering computing; sensor fusion; BP algorithm; fuzzy neural network; hydraulic braking system; hydraulic system; information fusion fault diagnosis system; multisensor information fusion; Fault diagnosis; Fuzzy logic; Fuzzy neural networks; Hydraulic systems; Mining equipment; Neural networks; Neurons; Sensor fusion; Sensor phenomena and characterization; Sensor systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia Workshop on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3490-9
  • Type

    conf

  • DOI
    10.1109/PACIIA.2008.406
  • Filename
    4756757