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Here, we show that a hybrid plasmonic/photonic crystal consisting of a thin layer of bioactive plasmonic material (i.e. silver) deposited on top a 1D PhC can detect one of the most common bacterial contaminant, namely Escherichia coli. We speculate that the change in the plasmon charge density brought about by metal/bacterium interaction results in a variation of the plasmon resonance that, in turns, translates in a shift of the photonic structural color.

Highly detailed computer models are required for design and development of modern flight control systems, capable of emulating with high accuracy the behaviour of on-board equipment. At the same time, different simplified models are needed, specifically intended for operations such as the optimization of preliminary design and the development of diagnostic or prognostic strategies. These simplified models are required to combine sufficient levels of accuracy and reliability with reduced computational costs, to minimize the computational burden associated with prognostic and optimization algorithms. In this work, we focus on electro-hydraulic actuators, since they are critical subsystems in terms of safety and availability of the aircraft. Advanced monitoring and prognostic algorithms require new numerical models, combining an acceptable computational effort with a satisfying ability to simulate their performance and dynamics. To this purpose, this paper proposes a new simplified numerical model of the servovalve fluid-dynamic behaviour. This numerical algorithm, based on a very compact semi-empirical formulation, is intended to take into account in a simplified but sufficiently accurate way several typical effects related to the SV spool geometry and the operating conditions. To evaluate the approximations introduced by this model into a system-level simulation, it has been integrated into a dedicated numerical model simulating a simple electrohydraulic on-board actuator, and compared with a higher fidelity servovalve model.

Electromechanical actuators (EMAs) based on Permanent Magnet Synchronous Motors (PMSMs) are currently employed on various aircraft systems, and are becoming more and more widespread in safety critical applications. Compared to other electrical machines, PMSM offer a high power to weight ratio and low cogging: this makes them suited for position control and actuation tasks. EMAs offer several advantages over hydraulic servoactuators, in terms of modularity, mechanical simplicity, overall weight and fuel efficiency. At the same time, their basic reliability is inherently lower compared to hydraulic actuators. Then, the use of EMAs for safety critical aircraft systems requires the adoption of risk mitigation techniques to counter this issue. In this framework, diagnostic and prognostic strategies can be used for the system health management, to monitor its behaviour in search of the early signs of the most common or dangerous failure modes. We propose a low fidelity model of a PMSM based EMA, intended for model-based diagnostic and prognostic monitoring. The model features low computational cost, allowing the execution in nearly real-time, combined with suitable accuracy in the simulation of faulty system operations. This simplified emulator is validated by comparing its behaviour to a higher fidelity model, employed as a simulated test bench.

Asymmetry limitation requirements between left and right wing flap surfaces play an important role in the design of the implementation of the secondary flight control system of modern airplanes. In fact, especially in the case of sudden breaking of one of the torsion bars of the flap transmission line, the huge asymmetries that can rapidly develop could compromise the lateral-directional controllability of the whole aircraft (up to cause catastrophic occurrences). Therefore, in order to guarantee the aircraft safety (especially during take-off and landing flight phase in which the effects of asymmetries could generate uncontrollable aircraft attitudes), it is mandatory to timely detect and neutralize these asymmetries. The current monitoring techniques generally evaluate the differential angular position between left and right surfaces and, in most the events, limit the Flaps Control System (FCS) asymmetries, but in severe fault conditions (e.g. under very high aerodynamic loads), unacceptable asymmetries could be generated, compromising the controllability of the aircraft. To this purpose, in this paper the authors propose a new active monitoring and control technique capable of detecting the increasing angular error between the different flap surfaces and that, after stopping the whole actuation system, acts on the portion of the actuation line still connected to the PDU to minimize the FCS asymmetries.

Aircraft systems are becoming more and more complex, as they are required to perform multiple functions. For example, smart systems need to be able to self-monitor their working parameters, in order to infer their health status. All these additional functions require the system to acquire a multitude of measurements; albeit sometimes it is possible to implement virtual sensor techniques, dedicate sensing hardware is usually needed. As a main drawback, the installation of the needed sensors adds up to the total complexity, weight, cost and failure rate of the system. In this context, minimally invasive sensors can be used to measure the system parameters with high spatial resolution and minimal added complexity. One key technology in this field is the Fiber Bragg Gratings (FBG) optical sensors, used to perform strain and temperature measurements. This work describes an experimental campaign intended to assess and validate several installation techniques for FBGs as strain sensors. Two test benches were developed for different measurement setups. One is intended for creep and repeatability tests of a FBG sensor glued at both ends; the other was used to compare point gluing and continuous gluing techniques on an aluminium beam subject to a bending load. Results are compared with numerical simulations of the structure and measurements performed with traditional strain gages.

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.

The complexity of aircraft systems is steadily growing, allowing the machine to perform an increasing number of functions; this can result in a multitude of possible failure modes, sometimes difficult to foresee and detect. A prognostic tool to identify the early signs of faults and perform an estimation of Remaining Useful Life (RUL) can allow adaptively scheduling maintenance interventions, reducing the operating costs and increasing safety [1-4]. A first step for the RUL estimation is an accurate Fault Detection & Identification (FDI) to infer the system health status, necessary to determine when the components will no more be able to match their requirements [5]. With a model-based approach, the FDI is a model-matching problem, intended to adjust a parametric Monitor Model (MM) to reproduce the response of the system. The MM shall feature a low computational cost to be executed iteratively on-board; at the same time, it shall be detailed enough to account for a several failure modes [6]. We propose the simplification of an Electromechanical Actuator (EMA) dynamical model [7] for model-based FDI, focusing on the BLDC motor and Power Electronics, which account for most the computational cost of the original high fidelity model.