A review of simplified servovalve models for digital twins of electrohydraulic actuators

The development and detail design of complex electrohydraulic actuators for aircraft flight controls require the use of accurate, high fidelity fluid-dynamic simulations in order to predict the behaviour of the system within its whole operating envelope. However, those simulations are usually computationally expensive, and simplified models are useful for the preliminary design phases and real-time health monitoring. Within this context, this work presents a review of low fidelity models for the fluid-dynamic behaviour of an electrohydraulic servovalve. Those are intended to run in real time as digital twins of the physical system, in order to enable the execution of diagnostic and prognostic algorithms. The accuracy of the simulations is assessed by comparing their results against a detailed, physics-based high fidelity model, which computes the response of the equipment accounting for the pressure-flow characteristics across all the internal passageways of the valve

Lumped parameters multi-fidelity digital twins for prognostics of electromechanical actuators

The growing affirmation of on-board systems based on all-electric secondary power sources is causing a progressive diffusion of electromechanical actuators (EMA) in aerospace applications. As a result, novel prognostic and diagnostic approaches are becoming a critical tool for detecting fault propagation early, preventing EMA performance deterioration, and ensuring acceptable levels of safety and reliability of the system. These approaches often require the development of various types of multiple numerical models capable of simulating the performance of the EMA with different levels of fidelity. In previous publications, the authors already proposed a high-fidelity multi-domain numerical model (HF), capable of accounting for a wide range of physical phenomena and progressive failures in the EMA, and a low-fidelity digital twin (LF). The LF is directly derived from the HF one by reducing the system degrees of freedom, simplifying the EMA control logic, eliminating the static inverter model and the three-phase commutation logic. In this work, the authors propose a new EMA digital twin, called Enhanced Low Fidelity (ELF), that, while still belonging to the simplified types, has particular characteristics that place it at an intermediate level of detail and accuracy between the HF and LF models. While maintaining a low computational cost, the ELF model keeps the original architecture of the three-phase motor and the multidomain approach typical of HF. The comparison of the preliminary results shows a satisfactory consistency between the experimental equipment and the numerical models

Learning for predictions: Real-time reliability assessment of aerospace systems

Prognostics and Health Management (PHM) 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 maintenance and the down time of equipment. A major challenge in system prognostics is the availability of accurate physics based representations of the grow rate of faults. Additionally, the analysis of data acquired during flight operations is traditionally time consuming and expensive. This work proposes a computational method to overcome these limitations through the dynamic adaptation of the state-space model of fault propagation to on-board observations of system’s health. Our approach aims at enabling real-time assessment of systems health and reliability through fast predictions of the Remaining Useful Life that account for uncertainty. The strategy combines physics-based knowledge of the system damage propagation rate, machine learning and real-time measurements of the health status to obtain an accurate estimate of the RUL of aerospace systems. The RUL prediction algorithm relies on a dynamical estimator filter, which allows to deal with nonlinear systems affected by uncertainties with unknown distribution. The proposed method integrates a dynamical model of the fault propagation, accounting for the current and past measured health conditions, the past time history of the operating conditions (such as input command, load, temperature, etc.), and the expected future operating conditions. The model leverages the knowledge collected through the record of past fault measurements, and dynamically adapts the prediction of the damage propagation by learning from the observed time history. The original method is demonstrated for the RUL prediction of an electromechanical actuator for aircraft flight controls. We observe that the strategy allows to refine rapid predictions of the RUL in fractions of seconds by progressively learning from on-board acquisitions

A genetic-based prognostic method for aerospace electromechanical actuators

Prior awareness of impending failures of primary flight command electromechanical actuators (EMAs) utilizing prognostic algorithms can be extremely useful. Indeed, early detection of the degradation pattern might signal the need to replace the servomechanism before the failure manifests itself. Furthermore, such algorithms frequently use a model-based approach based on a direct comparison of the real (High Fidelity) and monitor (Low Fidelity) systems to discover fault characteristics via optimization methods. The monitor model enables the gathering of accurate and exact data while requiring a minimal amount of processing. This work describes a novel simplified monitor model that accurately reproduces the dynamic response of a typical aerospace EMA. The task of fault detection and identification is carried out by comparing the output signal of the reference system (the high fidelity model) with that acquired from the monitor model. The Genetic Algorithm is then used to optimize the matching between the two signals by iteratively modifying the fault parameters, getting the global minimum of a quadratic error function. Once this is found, the optimization parameters are connected with the assumed progressive failures to assess the system's health. The high-fidelity reference model examined in this study is previously conceptualized, developed, implemented in MATLAB-Simulink and finally experimentally confirmed

Comparison of Metaheuristic Optimization Algorithms for Electromechanical Actuator Fault Detection

Model-based Fault Detection and Identification (FDI) for prognostics rely on the comparison between the response of the monitored system and that of a digital twin. Discrepancies among the behavior of the two systems are analyzed to filter out the effect of uncertainties of the model and identify failure precursors. A possible solution to identify faults is to leverage a model able to simulate faults: an optimization algorithm varies the faults magnitude parameters within the model to achieve the matching between the responses of the model and the actual system. When the algorithm converges, we can assume that the fault parameters that produce the best match between the system and its digital twin approximate the actual faults affecting the equipment. The choice of an optimization algorithm appropriate for the task is highly problem dependent. Algorithms for FDI are required to deal with multimodal objective functions characterized by poor regularity and a relatively high computational cost. Additionally, the derivatives of the objective function are not usually available and must be obtained numerically if needed. Then, we restrict our search for a suitable optimization algorithm to metaheuristic gradient-free ones, testing Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, Grey Wolf Optimization, Dragonfly Algorithm, and Whale Optimization Algorithm. Their performances on the considered problem were assessed and compared, in terms of accuracy and computational time

Design and development of innovative FBG-based fiber optic sensors for aerospace applications

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

Diagnostics of electro-mechanical actuators based upon the back-EMF reconstruction

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

Innovative actuator fault identification based on back electromotive force reconstruction

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

A first-order lumped parameters model of electrohydraulic actuators for low-inertia rotating systems with dry friction

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