• DocumentCode
    1898609
  • Title

    Research of Sensor Fault Diagnosis Method Based on Immune Network and Fuzzy ART Neural Network

  • Author

    Gu, Jihai ; Tian, Ye ; Jin, Xiangyang

  • Author_Institution
    Dept. of Mech. Eng., Harbin Univ. of Commerce, Harbin, China
  • Volume
    2
  • fYear
    2009
  • fDate
    10-11 Oct. 2009
  • Firstpage
    572
  • Lastpage
    575
  • Abstract
    A new immune network model and its diagnosis algorithm were studied. With the deficiency of model algorithm in the identification of sensor correlation, a new algorithm used in the quantitative extraction and recognition of sensor correlation were proposed based on Fuzzy ART neural network, of which the diagnosis system was consisted and the immune network. By the simulation of temperature sensor fault in certain thermal control system, the method was validated. The simulation result shows that the system could recognize and diagnose the faults accurately, regardless of single or multiple sensor faults. The accuracy of recognition and diagnosis is above 90 percent when the sensor output is less than plusmn5 percent deviation.
  • Keywords
    ART neural nets; artificial immune systems; computerised instrumentation; fault diagnosis; fuzzy neural nets; sensors; diagnosis system; fuzzy ART neural network; identification; immune network model; quantitative extraction; sensor correlation recognition; sensor fault diagnosis; sensor faults; temperature sensor fault; thermal control system; Biological system modeling; Cells (biology); Fault diagnosis; Fuzzy neural networks; Immune system; Neural networks; Sensor systems; Subspace constraints; Temperature sensors; Thermal sensors; Correlation identification; Diagnosis algorithm; Fault diagnosis; Fuzzy ART neural networks; Immune network model; Sensor;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
  • Conference_Location
    Changsha, Hunan
  • Print_ISBN
    978-0-7695-3804-4
  • Type

    conf

  • DOI
    10.1109/ICICTA.2009.373
  • Filename
    5287745