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

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

PROGNOSTICS OF AEROSPACE ELECTROMECHANICAL ACTUATORS USING THE FAILURE MAPS TECHNIQUE

The gradual deployment of Electro Mechanical Actuators (EMAs) as primary flight controls actuators, driven by the “more electric” approach, must be paired up with a solid prognostic background in order to overcome the limited experience and to support the system during his lifecycle. In fact, assessing EMAs actual states thanks to Prognostic and Health Monitoring (PHM) systems and detecting potential failures is crucial to guarantee the compliance to the relative safety requirements. The research activity described in this paper focuses on the development of a model-driven deterministic methodology based on Failure Modes Maps (FMMs). Thanks to data obtained through a Numerical Test Bench (NTB) and a Simplified Model (SM), the proposed prognostic algorithm is proved capable of detecting and identifying the source and magnitude of two different failures: rotor eccentricity and increased friction. After a short description of the implemented models and a general overview of typical EMA failure modes as well as FMMs development, the proposed algorithm is explained in detail. This is followed by a comprehensive study of the two simulated failures as well as the creation of the relative FMMs. Finally, the proposed prognostic algorithm is successfully applied on the obtained FMMs

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

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 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