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DALLA VEDOVA M D L , GERMANÀ A , BERRI P C , et al . Model-based fault detection and identification for prognostics of electromechanical actuators using genetic algorithms [J ] . Aerospace , 2019 , 6 ( 9 ): 94 . DOI: 10.3390/aerospace6090094 http://doi.org/10.3390/aerospace6090094 https://www.mdpi.com/2226-4310/6/9/94 https://www.mdpi.com/2226-4310/6/9/94 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.

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DI RITO G , LUCIANO B , BORGARELLI N , et al . Model-based condition-monitoring and jamming-tolerant control of an electro-mechanical flight actuator with differential ball screws [J ] . Actuators , 2021 , 10 ( 9 ): 230 . DOI: 10.3390/act10090230 http://doi.org/10.3390/act10090230 https://www.mdpi.com/2076-0825/10/9/230 https://www.mdpi.com/2076-0825/10/9/230 The work deals with the development of deterministic model-based condition-monitoring algorithms for an electromechanical flight control actuator with fault-tolerant architecture, in which two permanent magnets synchronous motors are coupled with differential ball screws in speed-summing paradigm, so that the system can operate even after a motor fault, an inverter fault or a mechanical jamming. To demonstrate the potential applicability of the system for safety-critical aerospace applications, the failure transients related to major fault modes have to be characterised and analysed. By focusing the attention to jamming faults, a detailed nonlinear model of the actuator is developed from physical first principles and experimentally validated in both time and frequency domains for normal condition and with different types of jamming. The validated model is then used to design the condition-monitoring algorithms and to characterize the system failure transient, by simulating mechanical blocks in different locations of the transmission. The operability after the fault, obtained via fault-tolerant control strategy and position regulator reconfiguration, is also verified, by highlighting and discussing possible enhancements and criticalities.

BALABAN E , SAXENA A , NARASIMHAN S , et al . Prognostic health-management system development for electromechanical actuators [J ] . Journal of Aerospace Information Systems , 2015 , 12 ( 3 ): 329 - 344 . DOI: 10.2514/1.I010171 http://doi.org/10.2514/1.I010171 https://arc.aiaa.org/doi/10.2514/1.I010171 https://arc.aiaa.org/doi/10.2514/1.I010171

BERRI P C , DALLA VEDOVA M D L , MAGGIORE P . A simplified monitor model for EMA prognostics [J ] . MATEC Web of Conferences , 2018 , 233 : 00016 . DOI: 10.1051/matecconf/201823300016 http://doi.org/10.1051/matecconf/201823300016 https://www.matec-conferences.org/10.1051/matecconf/201823300016 https://www.matec-conferences.org/10.1051/matecconf/201823300016 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.

ZHANG Y J , LIU L S , PENG Y , et al . An electro-mechanical actuator motor voltage estimation method with a feature-aided Kalman filter [J ] . Sensors , 2018 , 18 ( 12 ): 4190 . DOI: 10.3390/s18124190 http://doi.org/10.3390/s18124190 http://www.mdpi.com/1424-8220/18/12/4190 http://www.mdpi.com/1424-8220/18/12/4190 Electro-Mechanical Actuators (EMA) have attracted growing attention with their increasing incorporation in More Electric Aircraft. The performance degradation assessment of EMA needs to be studied, in which EMA motor voltage is an essential parameter, to ensure its reliability and safety of EMA. However, deviation exists between motor voltage monitoring data and real motor voltage due to electromagnetic interference. To reduce the deviation, EMA motor voltage estimation generally requires an accurate voltage state equation which is difficult to obtain due to the complexity of EMA. To address this problem, a Feature-aided Kalman Filter (FAKF) method is proposed, in which the state equation is substituted by a physical model of current and voltage. Consequently, voltage state data can be obtained through current monitoring data and a current–voltage model. Furthermore, voltage estimation can be implemented by utilizing voltage state data and voltage monitoring data. To validate the effectiveness of the FAKF-based estimation method, experiments have been conducted based on the published data set from NASA’s Flyable Electro-Mechanical Actuator (FLEA) test stand. The experiment results demonstrate that the proposed method has good performance in EMA motor voltage estimation.

CHIRICO A J III , KOLODZIEJ J R . A data-driven methodology for fault detection in electromechanical actuators [J ] . Journal of Dynamic Systems, Measurement, and Control , 2014 , 136 ( 4 ): 041025 . DOI: 10.1115/1.4026835 http://doi.org/10.1115/1.4026835 https://asmedigitalcollection.asme.org/dynamicsystems/article/doi/10.1115/1.4026835/371900/A-DataDriven-Methodology-for-Fault-Detection-in https://asmedigitalcollection.asme.org/dynamicsystems/article/doi/10.1115/1.4026835/371900/A-DataDriven-Methodology-for-Fault-Detection-in This research investigates a novel data-driven approach to condition monitoring of electromechanical actuators (EMAs) consisting of feature extraction and fault classification. The approach is able to accommodate time-varying loads and speeds since EMAs typically operate under nonsteady conditions. The feature extraction process exposes fault frequencies in signal data that are synchronous with motor position through a series of signal processing techniques. A resulting reduced dimension feature is then used to determine the condition with a trained Bayesian classifier. The approach is based on signal analysis in the frequency domain of inherent EMA signals and accelerometers. For this work, two common failure modes, bearing and ball screw faults, are seeded on a MOOG MaxForce EMA. The EMA is then loaded using active and passive load cells with measurements collected via a dSPACE data acquisition and control system. Typical position commands and loads are utilized to simulate “real-world” inputs and disturbances and laboratory results show that actuator condition can be determined over a range of inputs. Although the process is developed for EMAs, it can be used generically on other rotating machine applications as a Health and Usage Management System (HUMS) tool.

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