A possibilistic approach to causal diagnosis for engine dyno test benches
Résumé
Tracking malfunctions in reasonable time on an engine dyno test bench is becoming too complex for human beings. An online diagnosis system is being studied for this purpose. Knowledge about possible effects of a malfunction on an attribute and (imprecise) observations are modelled by possibility distributions. Any kind of attributes (binary, numerical, . . .) is allowed. The paper assumes the single-fault hypothesis. Two main indices are respectively used for respectively detecting i) the malfunctions whose presence is more or less inconsistent with the available observations and ii) the malfunctions (most of) all the expected effects of which have been more or less certainly observed (using abduction).