• DocumentCode
    807032
  • Title

    Fault diagnosis of electronic system using artificial intelligence

  • Author

    Fenton, Billy ; McGinnity, Martin ; Maguire, Liam

  • Author_Institution
    Ulster Univ., UK
  • Volume
    5
  • Issue
    3
  • fYear
    2002
  • fDate
    9/1/2002 12:00:00 AM
  • Firstpage
    16
  • Lastpage
    20
  • Abstract
    With increasing system complexity, shorter product life cycles, lower production costs, and changing technologies, the need for intelligent tools for all stages of a product´s lifecycle is becoming increasingly important. The purpose of this article is to give a brief review how AI has been used in the field of electronic fault diagnosis. Topics discussed include: rule-based diagnostic systems; model-based diagnostic systems; case-based reasoning (CBR); fuzzy reasoning and artificial neural networks (ANN); hybrid approaches; IEEE diagnostic standards and automated diagnostic tool future developments.
  • Keywords
    IEEE standards; automatic test equipment; case-based reasoning; diagnostic expert systems; diagnostic reasoning; electronic equipment testing; failure analysis; fault diagnosis; fuzzy logic; knowledge engineering; neural nets; AI; ANN; CBR; IEEE diagnostic standards; artificial intelligence; artificial neural networks; automated diagnostic tools; case-based reasoning; electronic system fault diagnosis; fuzzy logic; fuzzy reasoning; hybrid diagnostic systems; intelligent tools; model-based diagnostic systems; rule-based diagnostic systems; Artificial intelligence; Circuit faults; Circuit testing; Digital circuits; Fault diagnosis; Knowledge acquisition; Mathematical model; Predictive models; Sequential circuits; Telephony;
  • fLanguage
    English
  • Journal_Title
    Instrumentation & Measurement Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    1094-6969
  • Type

    jour

  • DOI
    10.1109/MIM.2002.1028367
  • Filename
    1028367