During the last decades, innovative aircraft health management systems have been receiving increasing interest from Original Equipment Manufacturers (OEMs) and aircraft operators. Their implementation could lead to substantial benefits: drastic cuts in turnaround time, operation costs, and Life Cycle Costs (LCCs) as well as sharp increases in system availability, safety, and reliability. An interconnectivity step-up is hence needed to guarantee a seamless data transfer. In this paper, an integrated open-source solution for reliable data transmission and near real-time graphical visualization is proposed. After a comprehensive calibration and verification campaign performed on a test stand, the overall system has been successfully validated on structural data measured using a network of Fiber Bragg Gratings (FBGs) mounted on a radio-controlled model aircraft. The result is an effective and robust system able to monitor near real-time critical parameters and health status of structures. With this system, the temperature and displacements of the structure can be displayed on a heat map arranged on a 3D model and visualized through a computer application on the ground. The proposed methodology can be applied to heterogeneous scenarios, ranging from maintenance planning activities to performance checks, providing an all-in-one solution for flight data management as well as other applications in the structural monitoring domain.
The growing adoption of electrical energy as a secondary form of onboard power leads to an increase of electromechanical actuators (EMAs) use in aerospace applications. Therefore, innovative prognostic and diagnostic methodologies are becoming a fundamental tool to early identify faults propagation, prevent performance degradation, and ensure an acceptable level of safety and reliability of the system. Furthermore, prognostics entails further advantages, including a better ability to plan the maintenance of the various equipment, manage the warehouse and maintenance personnel, and a reduction in system management costs. Frequently, such approaches require the development of typologies of numerical models capable of simulating the performance of the EMA with different levels of fidelity: monitoring models, suitably simplified to combine speed and accuracy with reduced computational costs, and high fidelity models (and high computational intensity), to generate databases, develop predictive algorithms and train machine learning surrogates. Because of this, the authors developed a high-fidelity multi-domain numerical model (HF) capable of accounting for a variety of physical phenomena and gradual failures in the EMA, as well as a low-fidelity counterpart (LF). This simplified model is derived by the HF and intended for monitoring applications. While maintaining a low computing cost, LF is fault sensitive and can simulate the system position, speed, and equivalent phase currents. These models have been validated using a dedicated EMA test bench, designed and implemented by authors. The HF model can simulate the operation of the actuator in nominal conditions as well as in the presence of incipient mechanical faults, such as a variation in friction and an increase of backlash in the reduction gearbox. Comparing the preliminary results highlights satisfactory consistency between the experimental test bench and the two numerical models proposed by the authors.
The deployment of electro-mechanical actuators plays an important role towards the adoption of the more electric aircraft (MEA) philosophy. On the other hand, a seamless substitution of EMAs, in place of more traditional hydraulic solutions, is still set back, due to the shortage of real-life and reliability data regarding their failure modes. One way to work around this problem is providing a capillary EMA prognostics and health management (PHM) system capable of recognizing failures before they actually undermine the ability of the safety-critical system to perform its functions. The aim of this work is the development of a model-based prognostic framework for PMSM-based EMAs leveraging a metaheuristic algorithm: the evolutionary (differential evolution (DE)) and swarm intelligence (particle swarm (PSO), grey wolf (GWO)) methods are considered. Several failures (dry friction, backlash, short circuit, eccentricity, and proportional gain) are simulated by a reference model, and then detected and identified by the envisioned prognostic method, which employs a low fidelity monitoring model. The paper findings are analysed, showing good results and proving that this strategy could be executed and integrated in more complex routines, supporting EMAs adoption, with positive impacts on system safety and reliability in the aerospace and industrial field.
Electro-Mechanical Actuators (EMAs) deployment as aircraft flight control actuators is an imperative step towards more electric concepts, which propose an increased electrification in aircraft subsystems at the expense of the hydraulic system. Despite the strong benefits linked to EMAs adoption, their deployment is slowed down due to the lack of statistical data and analyses concerning their often-critical failure modes. Prognostics and Health Management (PHM) techniques can support their adoption in safety critical domains. A very promising approach involves the development of model-driven prognostics methodologies based on metaheuristic bio-inspired algorithms. Evolutionary (Differential Evolution (DE)) and swarm intelligence (particle swarm (PSO), grey wolf (GWO)) methods are approached for PMSM based EMAs. Furthermore, two models were developed: a reference, high fidelity model and a monitoring, low fidelity counterpart. Several failure modes have implemented: dry friction, backlash, short circuit, eccentricity and proportional gain. The results show that these algorithms could be employed in pre-flight checks or during the flight at specific time intervals. Therefore, EMA actual state can be assessed and PHM strategies can provide technicians with the right information to monitor the system and to plan and act accordingly (e.g. estimating components Remaining Useful Life (RUL)), thus enhancing the system availability, reliability and safety.
This paper proposes an active monitoring strategy to control aircraft trailing-edge high-lift devices (flaps) asymmetry. A variety of system failures can cause asymmetry in the control surfaces, including the transmission torsion bar break down and control surface actuator wear and tear. The authors’ novel asymmetry active monitoring approach detects and identifies flaps position asymmetry. Once the failure side has been identified, the active control activates the wingtip brakes to stop the uncontrolled flap surface. The still controlled flaps are driven to the damaged surface braking point to reduce flap asymmetry. As a result, the undesired aircraft roll moment (due to flaps asymmetry) will be controlled, and the aircraft maneuverability after failure will be (partially) restored. The proposed asymmetry active monitoring technique has been widely tested in different operational and failure conditions, using wear-free or worn-out actuators and considering every failure side scenario. The behavior of the proposed active model is evaluated in terms of time response and stability margin under certain operating conditions.
The deployment of Electro-Mechanical Actuators plays an important role towards the adoption of the More Electric Aircraft (MEA) philosophy. On the other hand, a seamless substitution of EMAs in place of more traditional hydraulic solutions is still set back due to the shortage of real-life and reliability data regarding their failure modes. One way to work around this problem is providing a capillary EMA Prognostics and Health Management (PHM) system, capable of recognizing failures before they actually undermine the ability of the safety-critical system to perform its functions. The authors have developed a model-based prognostic framework for PMSM based EMAs leveraging a metaheuristic algorithm: Evolutionary (Differential Evolution (DE)) and swarm intelligence (particle swarm (PSO), grey wolf (GWO)) methods are considered. Several failures (dry friction, backlash, short circuit, eccentricity and proportional gain) are simulated thanks to a Reference Model, acting as a Numerical Test Bench, then detected and identified thanks to the envisioned prognostic method, which leverages a low fidelity Monitoring Model. The employed algorithms showed good results and prove that this strategy could be executed in pre-flight checks or during the flight at specific time intervals, with positive impacts on system safety and availability.
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