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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.

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.

This article describes a mitigation strategy against the risks potentially caused by RPAS capable of performing Specific Category of operations within not segregated airspace. A selection of hazards has been associated to these RPAS and it has been assessed thus obtaining a risk matrix. Considering the basic principles of risk analysis management based on Safety Management System theory, after having implemented the matrix (risks identification and ranking), a new mitigation strategy has been defined in order to maintain constantly the hazards consequences at or below an acceptable level. A strategy based on 'Expert Systems' has been chosen. 'Expert Systems' are computer systems capable of suggesting solutions to problems emulating human expertise in a given field of knowledge. There are many typologies of 'Expert Systems'; the ones considered in this article are the rule-based 'Expert Systems'. They are characterized by a basis of knowledge built from set of rules expressed as 'IF'-'THEN' statements. In this case, each statement has been directly implemented from the operational risks contained in the matrix with a one-to-one correspondence between mitigating rules and hazards for a given RPAS capable of performing Specific Category of operations. The novelty of this process is deemed to be the idea of laying down the basis for the implementation of a software based on artificial intelligence (the 'Expert System') to be integrated with the Flight Control System/Autopilot Subsystem of the RPASs object of the original risk assessment in order to recognize risks and promptly mitigate them during the execution of operational sorties within not segregated airspace. Two levels of integration (basic and advanced) are described and discussed in the article. [ABSTRACT FROM AUTHOR]