Proposal of a simplified Coulomb friction numerical model for the preliminary design of electrohydraulic servomechanisms

Electrohydraulic servomechanisms (EHAs) are particularly interesting for aviation application, in fact tanks to the high power to weight ration are widely diffused in medium to large cargo and passengers planes or fighters. This work is focused on the proposal of a new dry friction numerical algorithm, based upon Coulomb's approach, which can be integrated into simulation algorithms obtained by degrading the systems dynamic models (e.g. an overdamped second-order system reducible to a simpler first-order one). This approach, if correctly applied, significantly reduces the computational burden, without significant losses in simulation accuracy. The authors evaluated the approach proposed by a numerical test bench simulating the behaviour of an electrohydraulic linear actuator commonly used in primary flight controls

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

Proposal of a new simplified coulomb friction model applied to electrohydraulic servomechanisms

The design of electro-hydraulic servomechanisms characterized by high precision requirements generally needs adequate knowledge of its characteristics, and, in particular, of nonlinear phenomena. Among these, Coulomb's frictional forces acting on the mechanical elements in relative motion are critical to guarantee an implementation capable of respecting the accuracy requirements. The correct evaluation of this phenomenon allows understanding the behaviour of the physical system considered, to estimate its performance by implementing it in a simulation environment, and to design new devices taking into account the relative constraints. Accurate modelling and simulation of the considered system generally imply the use of high order dynamic models (typically, of second-order nonlinear or higher). However, under certain conditions, it is possible (and advisable) to simplify the mathematical structure of the numerical model, degrading it to a simple first-order, reducing its complexity and computational cost and, nevertheless, still obtaining results comparable with higher-order models. In this paper, the authors propose a new computational model capable of being implemented within these degraded numerical models, allowing them to simulate the main effects due to dry frictions (Coulomb's model). This first-order dynamic model is compared with the corresponding second-order ones to evaluate their performances in different scenarios

Thermomechanical calibration of FBG sensors for aerospace applications

Optical fibers have found widespread use in engineering, from communication to sensors. Among them, Fiber Bragg Gratings are allowed to detect several parameters. Scope of this work is to assess their performances as temperature and mechanical strain sensors for aerospace: in this regard, an experimental calibration is discussed. Then, alternative approaches are tested in order to distinguish thermal from mechanical contributes. This is first addressed by using a hybrid system of digital and optical sensors, and then then with a fully optical system. Both the presented solutions reached the scope. A concept of a third, innovative approach, is also described

Optimization methodologies study for the development of prognostic artificial neural network

In this work, we discuss the implementation and optimization of an artificial neural network (ANN) based on the analysis of the back-EMF coefficient capable of making electromechanical actuator (EMA) prognostics. Starting from the pseudorandom generation of failure values related to static rotor eccentricity and partial short circuit of the stator coils, we simulated through a MATLAB-Simulink model the values of currents, voltages, position and angular velocity of the rotor and thanks to these we obtained the back-electromotive force which represents the input layer of the ANN. In this paper, we will turn our attention to optimizing the hyperparameters which influence supervised learning and make it more performing in terms of computational cost and complexity. The results are satisfactory dealing with the number of examples present in the available dataset

Electrohydraulic Servomechanisms Affected by Multiple Failures: A Model-Based Prognostic Method Using Genetic Algorithms

In order to detect incipient failures due to a progressive wear of a primary flight command electro hydraulic actuator (EHA), prognostics could employ several approaches; the choice of the best ones is driven by the efficacy shown in failure detection, since not all the algorithms might be useful for the proposed purpose. In other words, some of them could be suitable only for certain applications while they could not give useful results for others. Developing a fault detection algorithm able to identify the precursors of the above mentioned EHA failure and its degradation pattern is thus beneficial for anticipating the incoming failure and alerting the maintenance crew so as to properly schedule the servomechanism replacement. The research presented in the paper was focused to develop a new prognostic procedure centered on the characterization of the state of health of a common electro-hydraulic actuator for primary command usage. It is based on an innovative model based fault detection and identification method (FDI) that makes use of deterministic and heuristic solvers in order to converge to the actual state of wear of the tested actuator. In particular, the proposed model takes in account three different types of progressive failures: the clogging of the first stage of the flapper-nozzle valve, the rising of friction between spool and sleeve and finally the rising of friction between jack and cylinder. To assess the robustness of the proposed technique, an appropriate simulation test environment was developed. The results showed an adequate robustness and confidence was gained in the ability to early identify an eventual EHA malfunctioning with low risk of false alarms or missed failures

Prognostics of aerospace electromechanical actuators: Comparison between model-based metaheuristic methods

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

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

A new genetic algorithm model-based prognostic approach applied to onboard electrohydraulic servomechanisms

The ever green solution of the electro hydraulic actuator (EHA) applications for the control of modern primary flight commands, justified by the superiority of hydraulic systems in furnishing more efficient solutions for power supplying in a controlled manner, brings us to focus on the need to make the EHA as efficient and reliable as possible. To this purpose, it must be noted that reliability of modern systems is increasingly more based on the valid support of diagnostics and prognostics; in fact, these two are the most robust instruments which mitigate life cycle costs without losing reliability and guarantee, in compliance with regulations, the bases for health management of integrated components, subsystems and systems. Developing a fault detection algorithm able to identify the precursors of EHA faults and their degradation patterns is thus beneficial for anticipating the incoming failure and alerting the maintenance crew so as to properly schedule the servomechanism replacement. About that, this paper proposes a new EHA model-based fault detection and identification method (FDI) that makes use of deterministic and heuristic solvers in order to converge to the actual state of wear of the tested actuator. The proposed FDI algorithm has been tested on three different types of progressive failures (the clogging of the first stage of the flapper-nozzle valve, the rising of friction between spool and sleeve and finally the rising of friction between jack and cylinder): to this purpose, a dedicated simulation test environment was developed. Results showed an adequate robustness and a suitable confidence was gained about its ability to early identify EHA malfunctions with low risk of false alarms or missed failures

Electrohydraulic actuators affected by multiple failures: Proposal of an alternative model-based prognostic paradigm

Onboard electrohydraulic actuator (EHA) applied to primary and secondary flight command, and in particular the servovalves (SVs) regulating their hydraulic power, are complex devices and can fail in several ways: servovalves are critical components of the hydraulic servos and their correct operation is mandatory to ensure the proper functioning of the controlled servosystem. For this reason, a continuous monitor is typically performed to detect a servovalve loss of operation, but this monitor falls short of recognizing other malfunctionings. Often, a progressive degradation of a servovalve occurs, which does not initially create an unacceptable behavior, but eventually leads to a condition in which the servovalve, and hence the whole servoactuator operation, is impaired. Developing a prognostic algorithm able to identify the precursors of a servovalve failure and its degradation pattern is thus beneficial for anticipating the incoming failure and alerting the maintenance crew such to properly schedule the servovalve replacement. This avoids a servovalve failure in service, thereby ensuring improved equipment availability and minimizing the impacts onto the logistic line. To this purpose, authors propose a new model-based fault detection and identification (FDI) technique able to perform an early detection of two of the most common types of SV progressive failures (dry friction acting on servovalve spool and contamination of the first stage filter). The robustness of the proposed technique has been assessed through a simulation test environment, built on the purpose. Such simulation has demonstrated that the methodology has adequate robustness; also, the ability to early identify an eventual malfunctioning has been proved with low risk of missed failures or false positives