A Model-Based Prognostic Framework for Electromechanical Actuators Based on Metaheuristic Algorithms

Abstract

The deployment of electro-mechanical actuators plays an important role towards the adoption of the more electric aircraft (MEA) philosophy. On the other hand, a seamless substitution of EMAs, in place of more traditional hydraulic solutions, is still set back, due to the shortage of real-life and reliability data regarding their failure modes. One way to work around this problem is providing a capillary EMA prognostics and health management (PHM) system capable of recognizing failures before they actually undermine the ability of the safety-critical system to perform its functions. The aim of this work is the development of a model-based prognostic framework for PMSM-based EMAs leveraging a metaheuristic algorithm: the evolutionary (differential evolution (DE)) and swarm intelligence (particle swarm (PSO), grey wolf (GWO)) methods are considered. Several failures (dry friction, backlash, short circuit, eccentricity, and proportional gain) are simulated by a reference model, and then detected and identified by the envisioned prognostic method, which employs a low fidelity monitoring model. The paper findings are analysed, showing good results and proving that this strategy could be executed and integrated in more complex routines, supporting EMAs adoption, with positive impacts on system safety and reliability in the aerospace and industrial field.

Multi-Objective Optimization of Two-Stage Helical Pairs in Helical Hydraulic Rotary Actuator Using Ensemble of Metamodels and NSGA-II

“…Atila et al [7] compared the strengths and weaknesses of various commonly used metaheuristic optimization methods in the context of gear design optimization. Additionally, beyond their application in the optimization of gear structures, metaheuristic algorithms have also been successfully utilized for fault diagnosis of electro-mechanical actuators [8], global optimization in power point tracking of partially shaded solar photovoltaic systems [9], and optimizing deep learning models for secure IoT environments [10]. Collectively, metaheuristic algorithms demonstrate superior performance and are broadly applicable to various types of engineering optimization designs.…”