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Abstract



Isolation of Multiple-faults with Generalized Fault-modes


Most AI approaches for fault isolation handle only the behavioral modes OK and NOT OK. To be able to isolate faults in components with generalized behavioral modes, a new framework is needed. By introducing domain logic and assigning the behavior of a component to a behavioral mode domain, efficient representation and calculation of diagnostic information is made possible. Diagnosing components with generalized behavioral modes also requires extending familiar characterizations. The characterizations candidate, generalized kernel candidate and generalized minimal candidate are introduced and it is indicated how these are deduced. It is concluded that neither the full candidate representation nor the generalized kernel candidate representation are conclusive enough. The generalized minimal candidate representation focuses on the interesting diagnostic statements to a large extent. If further focusing is needed, it is satisfactory to present the minimal candidates which have a probability close to the most probable minimal candidate. The performance of the fault isolation algorithm is very good, faults are isolated as far as it is possible with the provided diagnostic information.

Dan Sune

2002

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