Optimization techniques for prognostics of on-board electromechanical servomechanisms affected by progressive faults

In relatively recent years, electromechanical actuators (EMAs) have gradually replaced systems based on hydraulic power for flight control applications. EMAs 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, EMAs 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 introduce the use of optimization techniques in prognostic methods (e.g. model-based parametric estimation algorithms) and propose a new model-based fault detection and identification (FDI) method, based on Genetic Algorithms (GAs) optimization approach, able to perform an early identification of the aforesaid progressive failures, investigating its ability to timely identify symptoms alerting that a component is degrading.