FLEA

Model-based diagnosis typically uses
analytical redundancy to compare predictions
from a model against observations from the
system being diagnosed. However this
approach does not work very well when it is
not feasible to create analytic relations
describing all the observed data, e.g., for
vibration data which is usually sampled at
very high rates and requires very detailed
finite element models to describe its behavior.
In such cases, features (in time and frequency
domains) that contain diagnostic information
are extracted from the data. Since this is a
computationally intensive process, it is not
efficient to extract all the features all the time.
In this paper we present an approach that
combines the analytic model-based and
feature-driven diagnosis approaches. The
analytic approach is used to reduce the set of
possible faults and then features are chosen to
best distinguish among the remaining faults.
We describe an implementation of this
approach on the Flyable Electro-mechanical
Actuator (FLEA) test bed.