Diagnosis of industrial plants is a complex task, usually performed by
systems provided with the plant itself. However, these diagnostic sys
tems are not reliable when the working conditions are different from t
hose expected at design time. In this paper, we present OMISSYS (Oppor
tunistic Model-based diagnosIS SYStem), a system that can diagnose fau
lts in plants whose components are imperfectly described, and where da
ta are affected by uncertainty and imprecision. OMISSYS exploits the a
vailable knowledge and data to obtain a suboptimal diagnosis for the d
etected fault. We represent imperfection using uncertain-fuzzy numbers
, a formalism that can represent both uncertainty and imprecision. OMI
SSYS was tested on realistic examples and it efficiently produced sati
sfactory diagnoses. (C) EIsevier Science Inc. 1997.