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.
Senast uppdaterad: 2019-06-05