Machine learning-based synthesis of diagnostic algorithms for electromechanical actuators to improve the aerospace flight safety

IF 3.1 2区 物理与天体物理 Q1 ENGINEERING, AEROSPACE
G.S. Veresnikov , S.G. Bazhenov , I.G. Bashkirov , S.L. Chernyshev , V.I. Goncharenko , A.V. Skryabin , D.A. Petrov
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引用次数: 0

Abstract

The relevance of research aimed at developing diagnostic technologies for electromechanical actuators is due to the need to improve flight safety in conditions of increasing intensity of highly electrified aircraft and spacecraft operations. The paper discusses one of the promising approaches to electromechanical actuator health management, which involves the use of machine learning methods to synthesize health monitoring algorithms. Machine learning methods make it possible to build classification models based on empirical data, which are used to generate recommendations for making operational decisions. Empirical data, which is a source of valuable experience and the basis of a training sample necessary for formalizing patterns in classification models, can be formed as a result of life tests, mathematical modeling, and actuator operation. In order to improve the safety of space flights, the article focuses on the integration of electromechanical actuator mathematical model methods, optimal space filling, and machine learning. Optimal space filling methods are used to reduce the computational costs associated with representative training sampling. Examples of developing classification models are given to determine failures associated with changes in gear (backlash, Coulomb friction and viscous friction) which is the most critical actuator link. As a result of computational studies, the main advantages of the proposed approach to the synthesis of electromechanical actuator health assessment algorithms are shown.
基于机器学习的机电致动器诊断算法合成,提高航空航天飞行安全性
旨在开发机电致动器诊断技术的研究之所以具有现实意义,是因为在高度电气化的飞机和航天器运行强度不断增加的条件下,需要提高飞行安全性。本文讨论了机电致动器健康管理的一种有前途的方法,其中涉及使用机器学习方法来合成健康监测算法。机器学习方法可以根据经验数据建立分类模型,用于生成操作决策建议。经验数据是宝贵经验的来源,也是建立分类模型所需的训练样本的基础。为了提高太空飞行的安全性,文章重点介绍了机电致动器数学模型方法、最优空间填充和机器学习的整合。优化空间填充方法用于降低与代表性训练采样相关的计算成本。文章举例说明了如何开发分类模型,以确定与齿轮变化(反向间隙、库仑摩擦和粘性摩擦)相关的故障,这是最关键的致动器环节。计算研究的结果表明,所提出的综合机电致动器健康评估算法的方法具有主要优势。
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来源期刊
Acta Astronautica
Acta Astronautica 工程技术-工程:宇航
CiteScore
7.20
自引率
22.90%
发文量
599
审稿时长
53 days
期刊介绍: Acta Astronautica is sponsored by the International Academy of Astronautics. Content is based on original contributions in all fields of basic, engineering, life and social space sciences and of space technology related to: The peaceful scientific exploration of space, Its exploitation for human welfare and progress, Conception, design, development and operation of space-borne and Earth-based systems, In addition to regular issues, the journal publishes selected proceedings of the annual International Astronautical Congress (IAC), transactions of the IAA and special issues on topics of current interest, such as microgravity, space station technology, geostationary orbits, and space economics. Other subject areas include satellite technology, space transportation and communications, space energy, power and propulsion, astrodynamics, extraterrestrial intelligence and Earth observations.
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