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

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

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

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

Metaheuristic Bio-Inspired Algorithms for Prognostics: Application to On-Board Electromechanical Actuators

Metaheuristic bio inspired algorithms are a wide class of optimization algorithms, which recently saw a significant growth due to its effectiveness for the solution of complex problems. In this preliminary work, we assess the performance of two of these algorithms-Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)-for the prognostic analysis of an electro-mechanical flight control actuator, powered by a Brushless DC (BLDC) trapezoidal motor. We focus on the first step of the prognostic process, consisting in an early Fault Detection and Identification (FDI); our model-based strategy consists in using an optimization algorithm to approximate the output of the physical system with a computationally light Monitor Model

The thermal control system of NASA’s Curiosity rover: a case study

In any space mission, maintaining subsystems temperature within the allowed limits is a difficult challenge. Parts exposed to the Sun need to be cooled because temperatures rise extremely high, while parts not directly exposed to the Sun need to be heated, because temperatures can drop dramatically. The vacuum does not conduct heat, so the only way to transfer energy is through electromagnetic radiation, generated by the thermal motion of particles in matter. Operating on a planet surface allow convective dissipation and, to a lesser extent, conductive heat dissipation. Furthermore, Mars' thin atmosphere mitigates the strong temperature gradients that would occur in a vacuum. Nevertheless, external parts of the rover are exposed to temperature ranging between – 123°C - +40°C. In this paper, the thermal control system of NASA's Curiosity rover will be presented, analyzing the challenges of maintaining suitable operating conditions in Martian environment and the solutions adopted to allow safe operations