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In relatively recent years, electromechanical actuators have gradually replaced systems based on hydraulic power for flight control applications. Electromechanical servosystems are typically operated by electrical machines that transfer rotational power to the controlled elements (e.g. the aerodynamic control surfaces) by means of gearings and mechanical transmission. Compared to electrohydraulic systems, electromechanical actuators offer several advantages, such as reduced weight, simplified maintenance and complete elimination of contaminant, flammable or polluting hydraulic fluids. On-board actuators are often safety critical; then, the practice of monitoring and analyzing the system response through electrical acquisitions, with the aim of estimating fault evolution, has gradually become an essential task of the system engineering. For this purpose, a new discipline, called Prognostics, has been developed in recent years. Its aim is to study methodologies and algorithms capable of identifying such failures and foresee the moment when a particular component loses functionality and is no longer able to meet the desired performance. In this paper, authors have introduced the use of optimization techniques in prognostic methods (e.g. model-based parametric estimation algorithms) and have proposed a new model-based fault detection and identification method, based on Genetic Algorithms optimization approach, able to perform an early identification of the aforesaid progressive failures, investigating its ability to identify timely symptoms alerting that a component is degrading. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Traditional hydraulic servomechanisms for aircraft control surfaces are being gradually replaced by newer technologies, such as Electro-Mechanical Actuators (EMAs). Since field data about reliability of EMAs are not available due to their recent adoption, their failure modes are not fully understood yet; therefore, an effective prognostic tool could help detect incipient failures of the flight control system, in order to properly schedule maintenance interventions and replacement of the actuators. A twofold benefit would be achieved: Safety would be improved by avoiding the aircraft to fly with damaged components, and replacement of still functional components would be prevented, reducing maintenance costs. However, EMA prognostic presents a challenge due to the complexity and to the multi-disciplinary nature of the monitored systems. We propose a model-based fault detection and isolation (FDI) method, employing a Genetic Algorithm (GA) to identify failure precursors before the performance of the system starts being compromised. Four different failure modes are considered: dry friction, backlash, partial coil short circuit, and controller gain drift. The method presented in this work is able to deal with the challenge leveraging the system design knowledge in a more effective way than data-driven strategies, and requires less experimental data. To test the proposed tool, a simulated test rig was developed. Two numerical models of the EMA were implemented with different level of detail: A high fidelity model provided the data of the faulty actuator to be analyzed, while a simpler one, computationally lighter but accurate enough to simulate the considered fault modes, was executed iteratively by the GA. The results showed good robustness and precision, allowing the early identification of a system malfunctioning with few false positives or missed failures. [ABSTRACT FROM AUTHOR]