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In the last years, the layout of servomechanisms used in the aeronautical field to actuate the flight controls has changed radically and, nowadays electromechanical actuators (EMAs) are increasingly replacing the older hydraulic powered actuator types. The definition of special monitoring procedures, based on the analysis of the system response and aiming to evaluate the evolution of faults, represents an important task of the modern system engineering taking into account that onboard actuators are typically safety critical items. The present paper proposes a new prognostic procedure centered on the characterization of the state of health of an EMA used in aircraft primary flight controls. This approach, based on the innovative use of a model-based fault detection and identification method (FDI), identifies the actuator actual state of wear of the actuator analyzing proper system operational parameters, able to put in evidence the corresponding degradation path, by means of a numerical algorithm based on spectral analysis techniques. The proposed FDI algorithm has been tested in case of EMA affected by two progressive failures (rotor static eccentricity and stator phase turn-to-turn short-circuit), showing an adequate robustness and a suitable ability to early identify EMA malfunctions with low risk of false alarms or missed failures.

The ever increasing adoption of electrical power as secondary form of on-board power is leading to an increase in the usage of electromechanical actuators (EMAs). Thus, in order to maintain an acceptable level of safety and reliability, innovative prognostics and diagnostics methodologies are needed to prevent performance degradation and/or faults propagation. Furthermore, the use of effective prognostics methodologies carries several benefits, including improved maintenance schedule capability and relative cost decrease, better knowledge of systems health status and performance estimation. In this work, a novel, real-time approach to EMAs prognostics is proposed. The reconstructed back electromotive force (back-EMF), determined using a virtual sensor approach, is sampled and then used to train an artificial neural network (ANN) in order to evaluate the current system status and to detect possible coils partial shorts and rotor imbalances.

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