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Impact technologies have developed a robust modeling paradigm for actuator fault detection and failure prediction. This model-based approach to prognostics and health management (PHM) applies physical modeling and advanced parametric identification techniques, along with fault detection and failure prediction algorithms, in order to predict the time-to-failure for each of the critical, competitive failure modes within the system. Advanced probabilistic fusion strategies are also leveraged to combine both collaborative and competitive sources of evidence, thus producing more reliable health state information. These algorithms operate only on flight control command/response data. This approach for condition-based maintenance provides reliable early detection of developing faults. As an advantage over 'black-box' health-monitoring schemes, faults and failure modes are traced back to physically meaningful system parameters, providing the maintainer with invaluable diagnostic and prognostic information. The developed model-based reasoner was validated and demonstrated on an electromechanical actuator (EMA) provided by Moog, Inc.