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This paper is on the Failure Modes and Effects and Criticality Analysis and Fault Tree Analysis methodologies applied to the equipment and functional subsystems of Remotely Piloted Aircraft Systems (RPAS). Such aerial vehicles have been used almost exclusively for military purposes until the first decade of the 2000s. The debate then was focused both on technical and regulatory issues and research activities. Thanks to this renewed interest on unmanned systems and thanks to relatively recent improvements in information science, telecommunication, electronics and material science a strong awareness on the potential extension of unmanned technologies to civil applications arose up. A variety of economic benefits has been recognized by the aviation community from the civil use of RPAS, but, due to the absence of the pilot on board both military and civilian RPAS have always been relegated to fly into segregated airspaces. Technical potentialities of RPAS will be fully exploited integrating them into controlled airspaces in a reliable and safe way. This paper shows an example of application of FMECA and FTA to RPAS and discuss the most critical issues related to the performed analyses as well as possible future developments of this work.

Progressive failures affecting onboard electromechanical actuators (EMA), especially if related to primary flight commands, could be a critical issue for the aircraft reliability and, in the worst cases, could compromise its safety. In the last years strong interest is expected by the development of prognostic algorithms able to provide an early identification of the precursors of EMA progressive failures. In this work authors proposes a new prognostic method based on two artificial neural networks (ANN), a basic and an enhanced feedforward neural network, performing the fault detection and identification of two critical progressive faults often affecting the EMA brushless motor (i.e. turn-to-turn short circuit of a stator coil and rotor static eccentricity); in order to identify a suitable data set able to guarantee an affordable ANN classification, the said failures precursors are properly pre-processed by a Discrete Wavelet Transform, extracting several features used as input of the proposed prognostic algorithm.

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]