On the effects of monitoring system on redundant hydraulic flight controls in failure conditions

An active monitoring system is generally required to detect failures of the on-board hydraulic system and overcome its incorrect behaviour. Indeed, several kinds of failures can affect the flight control systems, and sometimes they can be safety-critical. This work is focused on electrohydraulic actuators equipped with proper equalization devices acting when failure modes are present. It studies the most commonly used architecture of redundancy based on the torque-sum arrangement (hydraulic motors generate torques that are summed within the gear reducer connecting the power drive unit with the motion transmission). To simulate and evaluate different behaviours of the system affected by various failure mode conditions, the author developed a dedicated computer program based on a physical-mathematical model of the whole actuation system. It is equipped with a hydraulic motor, electro-hydraulic servovalve, position feedback, and equalization control law. The author pointed out the limits of each kind of failure linked to the related equalization device and concluded that, in case of failure, the disengagement of the equalization device allows a correct actuation operation, thus preventing all critical situations. Equalization devices represent an obstacle because of the reduced contrasting action performed by the operational valve against the failed one

Numerical modelling and simulation for systems engineering applications: Novel methods in design, development, monitoring, diagnostics and prognostics

Systems engineering is an interdisciplinary field of engineering that focuses on designing and managing complex engineering systems over their whole life cycle. Issues such as requirements definition, reliability, logistic support, testing and evaluation, maintainability, and many other disciplines necessary for the successful system design, development, implementation, and decommission become more complicated when dealing with large or complex projects. To this purpose, overlapping several disciplines, systems engineering ensures that all main aspects of a project or a system are considered and correctly integrated into a complex. Several tools are used at various stages of the systems engineering process, depending on their application. In particular, modelling techniques and numerical simulation environments are gradually playing increasingly essential roles in this process, both in the early stages of conceptual design, preliminary draft, and system development, but also the design and tuning of control or monitoring systems and development of diagnostic and prognostic algorithms. These topics are now in the spotlight of the scientific community and arouse a growing interest in several industrial sectors (e.g., aerospace, automotive, automation, and more). Therefore, I believe that a collection of selected works that provide an overview of the state of the art and highlight the most recent and promising studies could be received with interest by the technical-scientific community. Subsequently, I decided to promote this special issue of the International Journal of Mechanics and Control (JoMaC) entitled "Numerical Modelling and Simulation for Systems Engineering Applications: Novel Methods in Design, Development, Monitoring, Diagnostics and Prognostics". This JoMaC Special Issue aims to collect innovative documents by proposing new approaches and original applications of numerical modelling and simulation techniques applied in the different fields of systems engineering. In this regard, I wish to thank prof. Andrea Manuello Bertetto, Editor in Chief of JoMaC, and the Editorial and Scientific Boards members that accepted and supported this project. Six valuable contributions, covering a wide range of disciplines and applications, are presented here. Giuseppe Petti et al. propose a new prognostic method based on an artificial neural network analysing back-EMF coefficients of electromechanical actuators (pp. 03-09) [1]. Vincenzo Niola et al. study the cavitation phenomena in a directional spool valve through the vibrational analysis of the acquired accelerometric signals (pp. 11-16) [2]. Luca Pugi et al. propose a new generation of electric directional drilling machines and develop simplified models for preliminary sizing, design, and calibration of these devices (pp. 17-29) [3]. Simone Arena et al. study an efficient distribution of the products of a delivery drugs Italian company by developing LRP analysis and finding solutions aimed at minimizing the total logistic system cost. (pp. 31-43) [4]. Mohammad Reza Homaeinezhad and M. Homaeinezhad examine a new electrical machine theory for simultaneous velocity and torque tracking control of the permanent magnet DC motors (pp. 45-59) [5]. Alberto Concu et al. show a prototype of a smart face mask, named AG47-SmartMask that, in addition to the function of both an active and passive anti-COVID-19 filter by an electro-heated filter brought to a minimum temperature of 38°C, also allows the continuous monitoring of numerous cardio-pulmonary variables (pp. 61-76) [6]. I wish to express my sincere appreciation to all researchers having contributed to this special issue, for their valuable support to the growth of this fascinating discipline

Model-Based Prognostic Methods Applied to Physical Dynamic Systems

In several engineering fields, especially in the recent years, the development of adequate diagnostic/prognostic methodologies able to provide a timely and reliable evaluation of the health status of a given system has become a strategic task in order to guarantee suitable levels of reliability, robustness and logistic availability. In particular, at this moment are in the spotlight some prognostic approaches that, on the basis of some representative parameters (measured directly or indirectly), are able to evaluate the health status of a physical system with a suitable (and quantifiable) level of accuracy and robustness; it must be noted that, especially in recent years, these methods are increasingly meeting interest and application in many technical fields and, nowadays, they represent an important task in various scientific disciplines. If considered failures are characterized to progressive evolutions, the health status of a given dynamic system (e.g. environmental, mechatronic, structural, etc.) and the related failure modes can be identified and quantified by means of different approaches widely described in the literature. In the last ten years more and more researchers studied and proposed new strategies aimed to design prognostic algorithms able to identify precursors of the progressive failures affecting a system: in fact, when a degradation pattern is correctly identified, it is possible to trigger an early warning and, if necessary, activate corrective actions (i.e. proper remedial or maintenance tasks, replacement of the damaged components, etc.). Typically these methods are strictly technology-oriented: they can result extremely effective for some specific applications whereas may fail for other purposes and technologies; therefore, it is necessary to "design" and calibrate the prognostic algorithm as a function of the considered problem, taking into account several parameters such as the given (dynamic) system, the available sensors (physical or virtual), the considered progressive failures and the related boundary conditions. This work proposes an overview of the most common model-based diagnostic/prognostic strategies (derived from aerospace systems field), putting in evidence their applicability, strengths and eventual shortcomings

Diagnostic/prognostics strategies applied to physical dynamic systems: A critical analysis of several model-based fault identification methods

The development of adequate diagnostic/prognostic methodologies, suitable to provide a timely and reliable evaluation of the health status of a given system on the basis of some representative parameters (measured in a direct or indirect way), is fundamentally started in engineering fields, but, especially in recent years, it is encountering more and more interest and application in many technical fields and nowadays it represents an important task in various scientific disciplines. The health status of a given dynamic system (e.g. environmental, mechatronic, structural, etc.) and the eventual incipient failures that concern it, especially if related to progressive evolutions, can be identified and quantified by means of different approaches widely described in the literature. It must be noted that, particularly in recent years, there has been a strong impulse in the development of strategies aimed to design prognostic algorithms able to identify precursors of the progressive failures affecting a system: in fact, if it is correctly identified the degradation pattern, an early warning can be triggered, leading to proper corrective actions (i.e. proper remedial or maintenance tasks, replacement of the damaged components, etc.). Since these algorithms are strictly technology-oriented, they can show great effectiveness for some specific applications, while they may fail for other applications and technologies: therefore, it is necessary to properly conceive the specific prognostic method as a function of several parameters such as the given (dynamic) system, the available sensors (physical or virtual), the considered progressive failures and the related boundary conditions. This work proposes a critical comparison between several diagnostic/prognostic strategies in order to put in evidence their strengths and the eventual shortcomings

Numerical modelling of sinusoidal brushless motor for aerospace actuator systems

The interest in electromechanical actuators (EMA) has been growing because of the development of next generation aircraft, based on the More Electric design. Electromechanical actuators have been gaining increased acceptance as they are becoming more and more safety-critical actuation devices: for prognostics and health management purposes of EMA, reliable and representative simulation models are needed in order to identify failures. This paper presents a multi domain model of EMA and it focuses on the numerical modelling of the Permanent Magnet Synchronous Motor (PMSM), also kwon as Sinusoidal Brushless Motor. The choice of the multi domain simulation is necessary to improve the simplifying hypotheses that are typically considered in numerical models and that are mostly used for prognostic analyses of electromechanical actuators

Optimization algorithms for prognostics of electrohydraulic on-board servomechanisms

This paper studies the response of an electrohydraulic actuator (EHA) subjected to three different progressive failures (demagnetization of the torque motor, increment of the jack static friction and presence of backlash); in particular, it is focused on the identification of failure precursors able to give an early identification of progressive failures affecting the system, in order to provide tools that can be used to predict its remaining useful life. This kind of analysis belongs to a new discipline, called Prognostics and Health Management (PHM), that focuses on predicting the time at which a system or a component will no longer perform its intended function, estimating its Remaining Useful Life (RUL) and, then, providing an effective diagnostic tool that allows them to exploit a component until it is safe, saving money. In order to conceive an effective prognostic algorithm authors studied the failures effects on the system behaviors, identifying some details in the monitored time-history signals that exclusively got evidence of a particular failure, avoiding confounding each other and allowing pointing out the fault level of the system. For this purpose, the authors developed a new EHA 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, the authors propose a new model-based fault detection and identification (FDI) method, based on Genetic Algorithms (GAs) optimization approach and parallelized calculations, investigating its ability to timely identify symptoms alerting that a component is degrading

Design and Development of a Planetary Gearbox for Electromechanical Actuator Test Bench through Additive Manufacturing

The development and validation of prognostic algorithms and digital twins for Electromechanical Actuators (EMAs) requires datasets of operating parameters that are not commonly available. In this context, we are assembling a test bench able to simulate different operating scenarios and environmental conditions for an EMA in order to collect the operating parameters of the actuator both in nominal conditions and under the effect of incipient progressive faults. This paper presents the design and manufacturing of a planetary gearbox for the EMA test bench. Mechanical components were conceived making extensive use of Fused Deposition Modelling (FDM) additive manufacturing and off-the-shelf hardware in order to limit the costs and time involved in prototyping. Given the poor mechanical properties of the materials commonly employed for FDM, the gears were not sized for the maximum torque of the electric motor, and a secondary torque path was placed in parallel of the planetary gearbox to load the motor through a disc brake. The architecture of the gearbox allowed a high gear ratio within a small form factor, and a bearingless construction with a very low number of moving parts

Optimization techniques for prognostics of on-board electromechanical servomechanisms affected by progressive faults

In relatively recent years, electromechanical actuators (EMAs) have gradually replaced systems based on hydraulic power for flight control applications. EMAs 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, EMAs 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 introduce the use of optimization techniques in prognostic methods (e.g. model-based parametric estimation algorithms) and propose a new model-based fault detection and identification (FDI) method, based on Genetic Algorithms (GAs) optimization approach, able to perform an early identification of the aforesaid progressive failures, investigating its ability to timely identify symptoms alerting that a component is degrading

Novel fluid dynamic nonlinear numerical models of servovalves for aerospace

Modern flight control system often requires the development of more and more highly detailed numerical simulation models in order to analyze their specific behavior as a whole or related to their components and subsystems. Especially during preliminary design activities or in the development of diagnostic or prognostic algorithms, it is often required to implement simplified numerical models able to simulate the actual behavior of the considered system, combining appropriate levels of accuracy and reliability with low calculation times and moderate computational efforts. In this work, authors investigated the feasibility of new simplified numerical models, aiming to provide faster models able to analyze the dynamic behavior of entire systems and, at the same time, able to guarantee a suitable level of accuracy. In particular, this paper concerns novel fluid-dynamics numerical models simulating the performance of servovalves. These algorithms are based upon a semi-empirical formulation and, although simplified, they are able to take calculate the effects of variable supply pressure and leakages (which is related to the control ports connecting the valve to the motor elements). Two new models are proposed and compared with a detailed reference. This comparison is performed by evaluating the performance of the different models and their ability to describe the fluid dynamic behavior of the considered valve

A New Prognostic Method Based on Simulated Annealing Algorithm to Deal with the Effects of Dry Friction on Electromechanical Actuators

In prognostics it is possible to apply several approaches with the aim to detect incipient failures, caused by progressive wear, of electromechanical actuators (EMA) in primary flight commands. The anticipation of a failure has to be performed through a correct interpretation of the degradation pattern, so to trig an early alert for maintenance and to properly schedule the servomechanism replacement. This paper proposes a prognostic approach based on the simulated annealing optimization method, able to identify symptoms of degradation before the behavior of the actuator becomes anomalous; friction failures are considered as the case study. The approach is validated through an experimental test bench, resulting in an adequate robustness and a high degree of confidence in the ability to early identify faults, with a low amount of false alarms or not annunciated failures