• DocumentCode
    2887439
  • Title

    Artificial intelligence approaches to fault diagnosis

  • Author

    Patton, R.J. ; Lopez-Toribio, C.J. ; Uppal, F.J.

  • Author_Institution
    Sch. of Eng., Hull Univ., UK
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    42491
  • Lastpage
    518
  • Abstract
    Fault diagnosis of control engineering systems can be based upon the generation of signals which reflect inconsistencies between the fault-free and faulty system operation-so-called residual signals. This paper outlines some recent approaches to the generation of residual signals using methods of integrating quantitative and qualitative system knowledge, based upon AI techniques
  • Keywords
    reviews; MIMO system; artificial intelligence approaches; control engineering systems; decision making; dynamic systems; fault diagnosis; fault isolation; fault tolerant control; fault-free operation; faulty system operation; fuzzy inference modelling; fuzzy logic; generation of signals; inconsistencies; neural networks; neuro-fuzzy systems; nonlinear systems; observers; parameter estimation; parity relations; qualitative system knowledge; quantitative system knowledge; residual signals; state space models;
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Condition Monitoring: Machinery, External Structures and Health (Ref. No. 1999/034), IEE Colloquium on
  • Conference_Location
    Birmingham
  • Type

    conf

  • DOI
    10.1049/ic:19990188
  • Filename
    772132