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, eg, 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 Electromechanical Actuator (FLEA) test bed.