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

FREEDOM: Validated Method for the Rapid Assessment of Incipient Multimodal Faults of Complex Aerospace Systems

Model-based Fault Detection and Isolation (FDI) methods allow to infer the health status of complex aerospace systems through a large quantity of data acquired in-flight, and evaluations of numerical models of the equipment. This results in an intensive computational procedure that can be addressed only grounding the aircraft. We introduce an original methodology to sensitively accelerate FDI by reducing the computational demand to identify the health status of the aircraft. Our scheme FREEDOM – Fast REliability Estimate and incipient fault Detection Of Multiphysics aerospace systems – proposes an original combination of a novel two-step compression strategy to compute offline a synthesized representation of the dynamical response of the system, and uses an inverse Bayesian optimization approach to infer online the level of damage determined by multiple fault modes affecting the equipment. We demonstrate and validate FREEDOM against numerical and physical experiments for the case of an ElectroMechanical Actuator (EMA) employed for secondary flight controls. Particular attention is dedicated to simultaneous incipient mechanical and electrical faults considering different experimental settings. The outcomes validate our FDI strategy, which permits to achieve the accurate identification of complex damages outperforming the computational time of state of the art algorithms by two orders of magnitude

Environmental Sensitivity of Fiber Bragg Grating Sensors for Aerospace Prognostics

Optical sensors have recently gained interest due to the many advantages they offer over traditional electrical sensors commonly used in aerospace applications. In particular, their total insensitivity to electromagnetic interference (EMI), the ease of multiplexing of different signals on a single line, the excellent resilience to hostile environments, the very compact dimensions, and the considerable overall weight savings resulting from the signal cables reduction, make technological solutions based on optical fibers a compelling alternative to traditional sensing elements. In this work, authors consider optical sensors based on Fiber Bragg Gratings (FBGs), which can reflect a very narrow band of wavelengths, called the Bragg wavelength, but are almost transparent for the other signals. This behaviour is obtained by realizing local variations of the refractive index of the FBG core. The Bragg wavelength, nominally defined in the production phase by the grating etching process, can vary as a function of physical changes in the sensor itself or environmental conditions (physical stresses applied to the grating or variations of temperature or humidity). The correlation of the Bragg wavelength variation with the physical variations of the sensor is essential to guarantee satisfactory levels of accuracy and reliability. In particular, using FBGs as mechanical strain sensors, it is crucial to estimate with proper accuracy the disturbance generated by environmental conditions and conceive an effective compensation method. Hence, this work studies the effects of environmental temperature and humidity variations on measurements, examining possible non-linear, time-dependent phenomena arising from the FBGs bonding. For this purpose, the authors developed a dedicated test bench to simultaneously detect the various physical measures (FBG deformation, temperature, humidity, Bragg wavelength variation), analyse their correlations, and formulate the said compensation strategy

Digital twins for prognostics of electro-hydraulic actuators: novel simplified fluid dynamic models for aerospace valves

In the design and development phases of electro-hydraulic actuators (EHAs) used for aircraft flight controls, it is often necessary to carry out accurate and high-fidelity fluid dynamics simulations to evaluate the system behaviour within its entire operating range and, if necessary, investigate its most critical issues. These high-fidelity simulations (nowadays achievable with different techniques and commercial software) generally become pretty expensive from a computational perspective. Therefore, especially in the preliminary design phases or implementing system health monitoring algorithms (in real-time), the need to adopt simplified models emerges definitely (albeit capable of guaranteeing the appropriate level of detail and accuracy). These simplified models are also essential for developing effective and reliable model-based prognostic strategies capable of performing early health assessments of EHA valves. This work proposes a new lumped-parameters simplified numerical model, which, despite having a very compact formulation and reduced computational costs, simulates the internal fluid dynamics of the valve, overcoming some critical issues typical of other models available in the literature. It evaluates valve performance as a function of spool position and environmental conditions (e.g. supply pressure), better-assessing flow rate feedback, internal leakages, and other operating conditions (e.g. spool fine adjustment, pressure supply variable, overpressure, or water hammer). The performance of this numerical model is evaluated comparing with other simplified models published in the literature. Moreover, it is validated with a high-fidelity digital twin that simulates the behaviour of the valve, taking into account the geometry of the spool, the properties of the hydraulic fluid, and the local internal fluid-dynamics (laminar or turbulent regime, cavitation, etc.)

A novel model-based metaheuristic method for prognostics of aerospace electromechanical actuators equipped with PMSM

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

Computational framework for real-time diagnostics and prognostics of aircraft actuation systems

Prognostics and Health Management (PHM) are emerging approaches to product life cycle that will maintain system safety and improve reliability, while reducing operating and maintenance costs. This is particularly relevant for aerospace systems, where high levels of integrity and high performances are required at the same time. We propose a novel strategy for the nearly real-time Fault Detection and Identification (FDI) of a dynamical assembly, and for the estimation of Remaining Useful Life (RUL) of the system. The availability of a timely estimate of the health status of the system will allow for an informed adaptive planning of maintenance and a dynamical reconfiguration of the mission profile, reducing operating costs and improving reliability. This work addresses the three phases of the prognostic flow - namely (1) signal acquisition, (2) Fault Detection and Identification, and (3) Remaining Useful Life estimation - and introduces a computationally efficient procedure suitable for real-time, on-board execution. To achieve this goal, we propose to combine information from physical models of different fidelity with machine learning techniques to obtain efficient representations (surrogate models) suitable for nearly real-time applications. Additionally, we propose an importance sampling strategy and a novel approach to model damage propagation for dynamical systems. The methodology is assessed for the FDI and RUL estimation of an aircraft electromechanical actuator (EMA) for secondary flight controls. The results show that the proposed method allows for a high precision in the evaluation of the system RUL, while outperforming common model-based techniques in terms of computational time.Comment: 57 page

A simplified monitor model for EMA prognostics

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

Computational framework for real-time diagnostics and prognostics of aircraft actuation systems

Prognostics and health management (PHM) are emerging approaches to product life cycle that will maintain system safety and improve reliability, while reducing operating and maintenance costs. This is particularly relevant for aerospace systems, where high levels of integrity and high performances are required at the same time. We propose a novel strategy for the nearly real-time fault detection and identification (FDI) of a dynamical assembly, and for the estimation of remaining useful life (RUL) of the system. The availability of a timely estimate of the health status of the system will allow for an informed adaptive planning of maintenance and a dynamical reconfiguration of the mission profile, reducing operating costs and improving reliability. This work addresses the three phases of the prognostic flow – namely (1) signal acquisition, (2) fault detection and identification, and (3) remaining useful life estimation – and introduces a computationally efficient procedure suitable for real-time, on-board execution. To achieve this goal, we propose to combine information from physical models of different fidelity with machine learning techniques to obtain efficient representations (surrogate models) suitable for nearly real-time applications. Additionally, we propose an importance sampling strategy and a novel approach to model damage propagation for dynamical systems. The methodology is assessed for the FDI and RUL estimation of an aircraft electromechanical actuator (EMA) for secondary flight controls. The results show that the proposed method allows for a high precision in the evaluation of the system RUL, while outperforming common model-based techniques in terms of computational time

A feasibility study of an artificial gravity system

Future crewed space exploration targets ambitious and distant destinations, requiring long duration missions that may largely affect the astronauts’ health condition. To limit these effects, spacecraft will require additional solutions for the support of human safety, health and quality of life. Among those, artificial gravity might introduce a disruptive development to allow manned space exploration to achieve broader frontiers, reducing bone and muscle deterioration, motion sickness, and fluid redistribution. This work proposes the preliminary design of a rotating gravity system developed to support long-duration manned missions with a healthy living environment for human comfort. The design problem considers different aspects of the possible missions: it includes the identification of key design drivers and mission requirements, along with the exploration and assessment of possible system architectures accounting for deployment and operation constraints. The design process relies on the use of Multidisciplinary Design Optimization (MDO) methodologies to account for the interaction of multiple disciplines at the conceptual stage, and to benefit from this knowledge for the identification of the best design solutions for the rotating gravity system. This approach allows to evaluate the effect of several design choice at an early stage into the system development, to inform critical trade-off decisions and determine the feasibility of such a system with technology available today or in the near future. Keywords: Artificial gravity, Multidisciplinary Design Optimization, Optimization

Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

Traditional hydraulic servomechanisms for aircraft control surfaces are being gradually replaced by newer technologies, such as Electro-Mechanical Actuators (EMAs). Since field data about reliability of EMAs are not available due to their recent adoption, their failure modes are not fully understood yet; therefore, an effective prognostic tool could help detect incipient failures of the flight control system, in order to properly schedule maintenance interventions and replacement of the actuators. A twofold benefit would be achieved: Safety would be improved by avoiding the aircraft to fly with damaged components, and replacement of still functional components would be prevented, reducing maintenance costs. However, EMA prognostic presents a challenge due to the complexity and to the multi-disciplinary nature of the monitored systems. We propose a model-based fault detection and isolation (FDI) method, employing a Genetic Algorithm (GA) to identify failure precursors before the performance of the system starts being compromised. Four different failure modes are considered: dry friction, backlash, partial coil short circuit, and controller gain drift. The method presented in this work is able to deal with the challenge leveraging the system design knowledge in a more effective way than data-driven strategies, and requires less experimental data. To test the proposed tool, a simulated test rig was developed. Two numerical models of the EMA were implemented with different level of detail: A high fidelity model provided the data of the faulty actuator to be analyzed, while a simpler one, computationally lighter but accurate enough to simulate the considered fault modes, was executed iteratively by the GA. The results showed good robustness and precision, allowing the early identification of a system malfunctioning with few false positives or missed failures.https://susy.mdpi