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.

In recent years aeronautical systems are becoming increasingly complex, as they are often required to perform various functions. New intelligent systems are required capable of self-monitoring their operation parameters, able to estimate their health status, and possibly perform diagnostic or prognostic functions. For these purposes, these systems frequently need to acquire several different signal types; although it is sometimes possible to implement virtual sensor techniques, it is usually necessary to implement dedicated sensing hardware. On the other hand, the installation of the required sensors can, however, significantly increase the complexity, the weight, the costs and the failure rate of the entire system. To overcome these drawbacks, new types of optical sensors, minimally invasive for measuring the system parameters and having a high spatial resolution and a minimum added complexity are now available. Fiber Bragg Gratings (FBGs) sensors are suitable for measuring various technical parameters in static and dynamic mode and meet all these requirements. In aerospace, they can replace several traditional sensors, both in structural monitoring and in other system applications, including mechatronic systems diagnostics and prognostics. This work reports the results of our experimental research aimed at evaluating and validating different FBG installation solutions such as deformation, bending, vibration, and temperature sensors. These were compared with numerical simulations results and measurements performed with traditional strain gauges and accelerometers.

The increasing interest for adopting electromechanical actuators (EMAs) on aircraft demands improved diagnostic and prognostic methodologies to be applied to such systems in order to guarantee acceptable levels of reliability and safety. While diagnostics methods and techniques can help prevent fault propagation and performance degradation, prognostic methods can be applied in tandem to reduce maintenance costs and increase overall safety by enabling predictive and condition-based maintenance schedules. In this work, a predictive approach for EMAs friction torque estimation is proposed. The algorithm is based on the reconstruction of the residual torque in mechanical transmissions. The quantity is then sampled and an artificial neural network (ANN) is used to obtain an estimation of the current health status of the transmission. Early results demonstrate that such an approach can predict the transmission health status with good accuracy.

Future generation actuation systems will be characterized by ever-increasing complexity. In this context, it will be necessary to adopt advanced health monitoring strategies to guarantee a high level of operational safety and system reliability. Prognostics and Health Management (PHM) is thus emerging as an enabling discipline for the design and operation of future advanced, complex systems. Smart systems with embedded self-monitoring capabilities are nowadays required in order to provide early faults identification and to perform innovative diagnostic and prognostic functions. In aerospace applications, the use of smart sensors could replace various types of traditional sensing elements, commonly used in structural monitoring with the additional capability of performing some prognostics or diagnostics tasks. This work proposes the first results of an experimental campaign aimed at evaluating and validating various packaging solutions for vibration amplification and detection using optical sensors (Fiber Bragg Gratings, FBGs), since characteristics frequencies can be good prognostics indicators of particular failure modes of a system. Several test samples were created by using 3D printed PLA and compared using a variety of bench tests. Results were compared in order to identify the strengths and weaknesses of the various proposed configurations, and were validated by comparing them with numerical simulations and experimental measurements performed with traditional sensors such as strain gages and accelerometers.

The ever increasing adoption of electrical power as secondary form of on-board power is leading to an increase in the usage of electromechanical actuators (EMAs). Thus, in order to maintain an acceptable level of safety and reliability, innovative prognostics and diagnostics methodologies are needed to prevent performance degradation and/or faults propagation. Furthermore, the use of effective prognostics methodologies carries several benefits, including improved maintenance schedule capability and relative cost decrease, better knowledge of systems health status and performance estimation. In this work, a novel, real-time approach to EMAs prognostics is proposed. The reconstructed back electromotive force (back-EMF), determined using a virtual sensor approach, is sampled and then used to train an artificial neural network (ANN) in order to evaluate the current system status and to detect possible coils partial shorts and rotor imbalances.

Adoption of electromechanical actuation systems in aerospace is increasing, and so reliable diagnostic and prognostics schemes are required to ensure safe operations, especially in key, safety-critical systems such as primary flight controls. Furthermore, the use of prognostics methods can increase the system availability during the life cycle and thus reduce costs if implemented in a predictive maintenance framework. In this work, an improvement of an already presented algorithm will be introduced, whose scope is to predict the actual degradation state of a motor in an electromechanical actuator, also providing a temperature estimation. This objective is achieved by using a properly processed back-electromotive force signal and a simple feed-forward neural network. Good prediction of the motor health status is achieved with a small degree of inaccuracy.