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
Link To Document :
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