• DocumentCode
    1587126
  • Title

    Fault diagnosis of a sewage plant

  • Author

    Schönwälder, J. ; Hofmann, M. ; Langendörfer, H.

  • Author_Institution
    Inst. fuer Betriebssyst. & Rechnerverbund, Tech. Univ., Braunschweig, Germany
  • fYear
    1991
  • Firstpage
    120
  • Lastpage
    123
  • Abstract
    A project whose aim is the development of an expert system for managing and diagnosing a sewage plant is presented. After a short description of how the knowledge acquisition process took place, the authors explain why the popular model-based diagnosis approach cannot be applied to the problem domain. Instead, they consider associative knowledge to solve the diagnostic problem. In order to adequately express knowledge about the structure of the sewage plant, knowledge about well understood subprocesses and associative knowledge for the diagnosis of the sewage plant, the authors designed the MOTESDM tool that supports hybrid knowledge representation. MOTESDM allows separation of associative knowledge from structural knowledge concerning the technical system
  • Keywords
    expert systems; high level languages; knowledge acquisition; knowledge representation; waste disposal; MOTESDM tool; associative knowledge; diagnostic problem; expert system; hybrid knowledge representation; knowledge acquisition process; problem domain; sewage plant; sewage plant fault diagnosis; structural knowledge; technical system; Artificial intelligence; Design engineering; Diagnostic expert systems; Fault diagnosis; Filters; Knowledge acquisition; Knowledge engineering; Plants (biology); Project management; Prototypes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence Applications, 1991. Proceedings., Seventh IEEE Conference on
  • Conference_Location
    Miami Beach, FL
  • Print_ISBN
    0-8186-2135-4
  • Type

    conf

  • DOI
    10.1109/CAIA.1991.120856
  • Filename
    120856