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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.

Prognostics and health management aim to predict the remaining useful life (RUL) of a system and to allow a timely planning of replacement of components, limiting the need for corrective maintenanc...

The prior knowledge of incipient failures of primary flight command electromechanical actuators (EMAs) with prognostic algorithms can be very beneficial. Indeed, early and proper detection and interpretation of the deterioration pattern can warn for replacing the servomechanism before the actual manifestation of the abnormal behaviour. Furthermore, such algorithms often exploit a model-based approach established on the direct comparison between the actual (High Fidelity) and the monitor (Low Fidelity) systems to identify fault parameters through optimization processes. The monitor model allows the acquisition of accurate and precise results with a contained computational effort. The authors developed a new simplified monitor model capable of faithfully reproducing the dynamic response of a typical aerospace EMA equipped with a Permanent Magnet Sinusoidal Motor (PMSM). This digital twin senses mechanical and electrical faults: friction, backlash, coil short circuit, static rotor eccentricity, and proportional gain. Fault detection and identification task are performed by comparing the output signal of the reference system (real or simulated) with the one obtained from the monitor model. After that, the Genetic Algorithm is chosen as the optimization algorithm to match the two signals by iteratively changing the fault parameters to detect the global minimum of a quadratic error function. Once a suitable fit is obtained, the corresponding optimization parameters are correlated with the considered progressive failures to evaluate the system’s health status. The high-fidelity reference models analysed in this work have been previously conceived, developed, implemented in Matlab-Simulink, and validated experimentally by researchers of the ASTRA group of the DIMEAS of Politecnico di Torino.

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.

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.

Purpose The purpose of this paper is to propose a new simplified numerical model, based on a very compact semi-empirical formulation, able to simulate the fluid dynamics behaviors of an electrohydraulic servovalve taking into account several effects due to valve geometry (e.g. flow leakage between spool and sleeve) and operating conditions (e.g. variable supply pressure or water hammer). Design/methodology/approach The proposed model simulates the valve performance through a simplified representation, deriving from the linearized approach based on pressure and flow gains, but able to evaluate the mutual interaction between boundary conditions, pressure saturation and leak assessment. Its performance was evaluated comparing with other fluid dynamics numerical models (a detailed physics-based high-fidelity one and other simplified models available in the literature). Findings Although still showing some limitations attributable to its simplified formulation, the proposed model overcomes several deficiencies typical of the most common fluid dynamic models available in the literature, describing the water hammer and the nonlinear dependence of the delivery differential pressure with the spool displacement. Originality/value Although still based on a simplified formulation with reduced computational costs, the proposed model introduces a new nonlinear approach that, approximating with suitable precision the pressure-flow fluid dynamic characteristic of a servovalve, overcomes the shortcomings typical of such models.

Real-time health monitoring of mechatronic onboard systems often involves model-based approaches comparing measured (physical) signals with numerical models or statistical data. This approach often requires the accurate measurement of specific physical quantities characterizing the state of the real system, the command inputs, and the various boundary conditions that can act as sources of disturbance. In this regard, the authors study sensor fusion techniques capable of integrating the information provided by a network of optical sensors based on Bragg gratings to reconstruct the signals acquired by one or more virtual sensors (separately or simultaneously). With an appropriate Fiber Bragg Gratings (FBGs) network, it is possible to measure directly (locally) several physical quantities (e.g. temperature, vibration, deformation, humidity, etc.), and, at the same time, use these data to estimate other effects that significantly influence the system behavior but which, for various reasons, are not directly measurable. In this case, such signals could be “virtually measured” by suitably designed and trained artificial neural networks (ANNs). The authors propose a specific sensing technology based on FBGs, combining suitable accuracy levels with minimal invasiveness, low complexity, and robustness to EM disturbances and harsh environmental conditions. The test case considered to illustrate the proposed methodology refers to a servomechanical application designed to monitor the health status in real-time of the flight control actuators using a model-based approach. Since the external aerodynamic loads acting on the system influence the operation of most of the actuators, their measurement would be helpful to accurately simulate the monitoring model's dynamic response. Therefore, the authors evaluate the proposed sensor fusion strategy effectiveness by using a distributed sensing of the airframe strain to infer the aerodynamic loads acting on the flight control actuator. Operationally speaking, a structural and an aerodynamic model are combined to generate a database used to train data-based surrogates correlating strain measurements to the corresponding actuator load.

Prognostics and Health Monitoring (PHM) is a discipline aiming to determine in advance the Remaining Useful Life (RUL) of a system. To do so, the operation of the system is monitored in search of the early signs of degradation and incipient faults; then, a model for the propagation of faults is employed to estimate the propagation of damages and evaluate the RUL. Usually, a fault threshold is employed as a stopping criterion for the evaluation of damage propagation, but this is not a reliable method when dealing with multiple faults affecting the system at the same time. Specifically, the combined effect of two fault modes can cause the system not to meet its requirements well before the single faults reach their individual thresholds. In this work, we address a model-based strategy to estimate whether the system with incipient faults is still able to meet its performance requirements. The method is applied to aerospace actuators, and performance is evaluated in terms of dynamical response. This model-based algorithm is too slow to be evaluated in real-time, so a Support Vector Machine (SVM) is trained as a surrogate function to speed up the computation. The results and computational times of the full, physics based model and those of its surrogate are compared and discussed.

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.

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 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.

In aerospace engineering, there are several control systems affected by dry friction, which are characterized by low inertia and high working frequencies. For these systems, it is possible to use a downgraded, first-order dynamic model to represent their behaviour properly without run into numerical problems that would be harmful for the solution itself and would require high computational power to be solved, which means more weight, costs, and complexity. Yet, the effect of dry friction is still possible to be accounted for accurately using a new algorithm based on the Coulomb friction model applied to the downgraded, first-order dynamic model. In this paper, the degraded first-order model is applied to an electrohydraulic servomechanism with its PID control unit, hydraulic motor, electrohydraulic servo-valve, and applied load. These components represent a classic airplane actuator system. The downgraded model will be compared to the second-order one focusing on the pros and cons of the reduction process with a focus on the effect of dry friction for reversible and irreversible actuators.

In recent years, optical-based sensors have sparked interest for the many advantages over traditional, electrical-based sensors, such as EMI insensitivity, ease of multiplexing on a single line, resilience to hostile environment and very compact size and global weight saving due to signal cables reduction. Considering said properties, optical sensors offer a compelling alternative to traditional sensing elements. One type of optical sensor is the Fiber Braggs Gratings sensors (FBG), which is a type of sensor that reflects a very narrow band of wavelengths, called Bragg wavelength, while being transparent for others; this behavior is achieved by local variations of the core refractive index. The Bragg wavelength can be easily correlated with physical changes in the sensor itself, due to either physical strain or temperature variation. It should be noted that the achievable measurement accuracy is thus comparable to the Bragg wavelength. However, for any practical application, FBGs need to be bonded to a support or surface; in this case, there is a lack of understanding of the effects of temperature and humidity variations on the combined sensor-glue system. In this work, a setup, intended to characterize the sensitivity of the fiber-glue combination to humidity and temperature will be presented.

Electrical systems are gradually replacing the more traditional hydraulic and pneumatic solutions for the transmission of secondary energy for onboard aircraft equipment. Therefore fault detection and health management strategies properly conceived for electrical devices are becoming a highly relevant topic for research and development in the aerospace disciplines. One possible practical implementation of these methodologies would be the identification of parameters for diagnostic and prognostic monitoring, which are highly sensitive to incipient faults but, at the same time, are less influenced by operating conditions (external loads, command input, temperatures, etc.). In this paper, the authors evaluated the effectiveness of counter-electromotive force (back-EMF) coefficient as a prognostic parameter, emphasizing a novel sampling approach that significantly lower the computational effort required while maintaining a good back-EMF coefficient curve reconstruction. The approach is virtual sensor-like, using only already available data for the correct operation of the BLDC motor. The proposed method was tested by evaluating the back-EMF coefficient reconstruction as a function of some progressive failures typical of EMA motors, such as inter-turn partial shorts and rotor static eccentricity. Its robustness to external disturbances has been tested by evaluating different actuation commands and operating conditions. As expected, the back-EMF signal shows a marked dependence on the considered failure modes and, at the same time, a suitable insensitivity to the other external factors.

Planetary gear drives are widely employed in electrical and hydraulic actuation systems, to adapt a high speed, low torque motor to a low speed, high torque user, within strict weight and volume constraints. During the early design phases of these devices, accurate yet simple simulation models are required to evaluate the performance of a given configuration of the device. Similar models are also useful within diagnostic and health monitoring analyses of existing machines, as a discrepancy between the actual behaviour of the physical system and that predicted by its digital twin may be the effect of a damage. This work compares different models available in literature for the efficiency of high gear ratio mechanical transmissions; the models are applied to multiple arrangements common for planetary drives, and the results in form of an efficiency map for the transmissions are compared and discussed. The simulations provide different levels of detail, and require different levels of knowledge about the specific architecture of the system. All of them are able to deal with dry friction; additionally, the different behaviour of the transmission under the effect loads aligned in the same direction of speed or in the opposite one is accounted for.

Electro Hydraulic Actuators (EHAs) keep their role as the leading solution for the control of current generation primary flight control systems: the main reason can be found in their high power to weight ratio, much better than other comparable technologies. To enhance efficiency and reliability of modern EHAs, it is possible to leverage the diagnostics and prognostics disciplines; these two tools allow reducing life cycle costs without losing reliability, and provide the bases for health management of integrated systems, in compliance with regulations. This paper is focused on the development of a fault detection algorithm able to identify the early signs of EHA faults, through the recognition of their precursors and related degradation patterns. Our methodology provides the advantage of anticipating incoming failures, triggering proper alerts for the maintenance team to schedule adequate corrective actions, such as the replacement of the degraded component. A new EHA model-based fault detection and identification (FDI) method is proposed; it is based on deterministic and heuristic solvers able to converge to the actual state of wear of the tested actuator. Three different progressive failure modes were chosen as test cases for the proposed FDI strategy: clogging of the first stage of the flapper-nozzle valve, spool-sleeve friction increase, and jack-cylinder friction increase. A dedicated simulation model was created for the purpose. The results highlighted that the method is adequate in robustness, since EHA malfunctions were identified with a low occurrence of false alarms or missed failures.

In recent years, aerospace systems have undergone a huge technical-scientific impulse, evolving new high-performance intelligent solutions able to perform diagnostic and prognostic functions autonomously. In this scenario, an important role is certainly played by the new types of sensors combining high performance (in terms of sensibility, accuracy, and reliability) with a marked resilience to external disturbances (e.g. EM noise or electrostatic discharges) and other environmental factors. Fiber Bragg Gratings (FBGs)sensors, suitable for measuring various engineering parameters in both static and dynamic modes, meet all these requirements; in aerospace, they could replace several traditional sensors, not only in structural monitoring but also in a much wider range of system applications including the diagnostic and prognostic of mechatronic devices. In this paper, the authors propose the first results of their investigation about the use of optical sensors for Electromechanical (EMA) and Electrohydrostatic Actuators (EHA). FBGs allow determination of aerodynamic hinge loads and minimally invasive measurements of local temperatures: the firsts are needed for in-flight model-based prognostics, whereas the detection of local heating allows to early infer an incipient partial short circuit of EM motor, a progressive power electronics failure or abnormal friction dissipation e.g. at bearing level. In order to assess the capabilities of optical sensors, evaluating different installation techniques and defining suitable configurations for the aforementioned mechatronic systems, a dedicated test bench has been developed and used for calibrating FBGs, analyzing their behaviour in different conditions of strain and temperature and conceiving a proper thermo-mechanical compensation strategy.

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.

In the last generation aircraft, the architecture of the powered flight control system adopted Electromechanical Actuators (EMAs). Being some on-board actuator safety critical, the practice of monitoring their behavior to determine their health condition is a task of growing importance. The choice of the best prognostic algorithm is driven primarily by their effectiveness in correctly identifying the health conditions of the system since each technique might be more or less useful in a given situation. In this contest, the authors propose a new GA-based fault detection tool, relying on a model-based approach, comparing the system output to that of a Monitor Model (MM), which is able to reproduce accurately the dynamic response of the actual EMA in terms of position, speed and equivalent current, even under the effects of different failure modes while keeping a reasonably low computational cost; this Fault Detection and Identification (FDI) algorithm have been extended to seven progressive failures. A numerical simulation test environment has been developed to simulate progressive faults and to evaluate the accuracy of this prognostic method. Results showed an adequate robustness and a suitable ability to early identify malfunctions with low risk of false alarms or missed failures. Moreover, the effect of a failures different from those considered was studied, to avoid safety concerns related to the missed identification of an incipient failure, hidden by another unknown failure mode.

Metaheuristic bio inspired algorithms are a wide class of optimization algorithms, which recently saw a significant growth due to its effectiveness for the solution of complex problems. In this preliminary work, we assess the performance of two of these algorithms - Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) - for the prognostic analysis of an electro-mechanical flight control actuator, powered by a Brushless DC (BLDC) trapezoidal motor. We focus on the first step of the prognostic process, consisting in an early Fault Detection and Identification (FDI); our model-based strategy consists in using an optimization algorithm to approximate the output of the physical system with a computationally light Monitor Model.

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.

The architecture of the actuation systems implemented in aeronautics in order to actuate the flight controls is changing radically and, in the last years, electromechanical actuators (EMAs) are gradually replacing the older type of actuators based on the hydraulic power. Given that some onboard actuators are safety critical, the practice of monitoring and analyzing the system's response (through electrical acquisition) and, then, to provide an evaluation of the evolution of the fault has gradually become an important task of the system engineering. To this purpose, a new discipline was born, called Prognostics, aiming to predict the moment in which a certain component loses its functionality and is not further able to meet desired performances. Given that many prognostic algorithms rely on model-based approaches (e.g. directly comparing the monitor with the real system or using it to identify the fault parameters by means of optimization processes), the design and development of appropriate monitoring models, able to combine simplicity, reduced computational effort and a suitable level of sensitivity and accuracy, becomes a fundamental step of the prognostic process. To this purpose, authors developed a new EMA Monitor Model able to reproduce the dynamic response of the actual system in terms of position, speed and equivalent current, even with the presence of incipient faults. Starting from this Monitor Model, authors propose a new model-based fault detection and identification (FDI) method, based on Genetic Algorithms optimization approach and parallelized calculations, investigating its ability to timely identify symptoms alerting that a component is degrading. The proposed FDI algorithm has been tested on six different progressive failures (dry friction torques and backlash affecting the mechanical transmission, turn to turn short circuit affecting the coils of the three stator phases and rotor eccentricity), simulating progressive faults and evaluating its accuracy. Results showed an adequate robustness and a suitable ability to early identify EMA malfunctions with low risk of false alarms or missed failures.

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.

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.

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.