• DocumentCode
    296929
  • Title

    An approach to fault diagnosis in dynamic systems using Kohonen neural networks

  • Author

    Ficola, Antonio ; Cava, Michele La ; Magnino, Fabio

  • Author_Institution
    Istituto di Elettronica, Perugia Univ., Italy
  • Volume
    1
  • fYear
    34881
  • fDate
    10-14 Jul1995
  • Firstpage
    166
  • Abstract
    In this paper, a fault diagnosis system for linear dynamic systems based on Kohonen neural networks is proposed. The technique of pattern recognition is taken into account for the classification of the modes of operation of the system. The pattern is given by the coefficients of the transfer matrix which are estimated by a least squares algorithm; in this way classification can also be achieved under dynamic conditions. The method employs an unsupervised neural network based on competitive learning. An example is proposed to show the effectiveness of this approach
  • Keywords
    fault diagnosis; least squares approximations; linear systems; pattern classification; self-organising feature maps; transfer function matrices; unsupervised learning; Kohonen neural networks; competitive learning; fault diagnosis; least squares algorithm; linear dynamic systems; operation mode classification; pattern recognition; transfer matrix coefficients; unsupervised neural network; Electronic mail; Fault detection; Fault diagnosis; Feature extraction; Intelligent networks; Least squares approximation; Neural networks; Parameter estimation; Pattern recognition; State estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics, 1995. ISIE '95., Proceedings of the IEEE International Symposium on
  • Conference_Location
    Athens
  • Print_ISBN
    0-7803-7369-3
  • Type

    conf

  • DOI
    10.1109/ISIE.1995.496495
  • Filename
    496495