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航空发动机振动监测与故障诊断技术研究进展 被引量:2

Research progress on vibration monitoring and fault diagnosis for aero-engine
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摘要 航空发动机汇集各领域高精尖技术,是国家科技、工业和国防实力的综合体现。复杂结构与恶劣服役环境致使其故障频发,发动机故障诊断与健康管理技术成为保障其安全、可靠运行的重要支撑。由于振动类故障是航空发动机的主要故障模式,本文从整机振动监测与故障诊断的系统研制与应用、理论研究现状及发展方向3个方面,对国内外现有航空发动机振动类故障诊断技术进行梳理、剖析,具体包括动力学分析、信号处理及深度学习等相关技术,分析航空发动机振动类故障诊断面临的问题与挑战,并归纳未来发展趋势。 Aeroengine amalgamates state-of-the-art technologies across diverse domains,serving as a comprehen⁃sive manifestation of a nation’s prowess in science and industry.Frequent malfunctions occur due to its complicated structure and harsh service environment.Therefore,it is essential to employ prognostic and health management tech⁃nology to provide crucial support for aviation safety and reliable operations.As vibration faults constitute a primary fail⁃ure mode in aeroengine,this paper,grounded in this premise,systematically reviews and analyzes existing vibration monitoring and fault diagnosis for aviation engines both domestically and internationally.The analysis is categorized into three dimensions,covering the application of overall vibration monitoring and diagnostic systems,typical fault characteristics and diagnostic methods,and the overall vibration fault diagnostic technology,including dynamic analy⁃sis,signal processing techniques,and relevant technologies such as deep learning.Then,the problems and chal⁃lenges faced by the existing vibration fault diagnosis of aeroengine are identified.Furthermore,future development goals are provided.
作者 胡明辉 高金吉 江志农 王维民 邹利民 周涛 凡云峰 王越 冯家欣 李晨阳 HU Minghui;GAO Jinji;JIANG Zhinong;WANG Weimin;ZOU Limin;ZHOU Tao;FAN Yunfeng;WANG Yue;FENG Jiaxin;LI Chenyang(State Key Laboratory of High-end Compressor and System Technology,Beijing University of Chemical Technology,Beijing 100029,China;Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education,Beijing University of Chemical Technology,Beijing 100029,China;Beijing Key Laboratory of Health Monitoring and Self-Recovery for High-end Mechanical Equipment,Beijing University of Chemical Technology,Beijing 100029,China)
出处 《航空学报》 EI CAS CSCD 北大核心 2024年第4期1-29,F0002,共30页 Acta Aeronautica et Astronautica Sinica
基金 国家自然科学基金(92160203) 特殊领域青年人才托举工程(2022-JCJQ-QT-059) 装备预研教育部联合基金(8091B022203)。
关键词 航空发动机 故障诊断 振动分析 动力学模型 信号处理 智能诊断 aeroengine fault diagnosis vibration analysis dynamical model signal processing intelligent diagnosis
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