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航空发动机主轴轴承状态监测研究现状与发展趋势 被引量:17

Research status and development trend of condition monitoring on main-shaft bearings used in aircraft engines
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摘要 航空发动机主轴轴承承受着高温、高速、重载、贫油、断油等极端工况,其疲劳、磨损等失效问题严重影响发动机的可靠性。因此,对航空发动机主轴轴承的使用状态进行有效精确监测极为重要。对航空发动机主轴轴承工况特点、主要失效模式和失效机制进行了梳理;针对主轴轴承的状态监测方法和技术,总结并对比分析了现有主轴轴承振动、滑油状态、声音、声发射、温度等监测方法的优势与不足;讨论了基于多传感器信息融合的主轴轴承状态监测方法及技术特色。结果表明:主轴轴承的材料、结构特性等对传感器输出信号的影响,传感器结构的微型化、无线化,高效的多传感器信息融合与决策方法,以及物理模型与数字模型的数据交互将成为主轴轴承状态监测未来主要的研究方向。 The main-shaft bearings in aircraft engines generally endure extreme operational conditions, i. e., high temperature, high speed, heavy load, poor oil and oil cut-off. Fatigue and wear among other failures in the main-shaft bearings significantly influence the reliability of the aircraft engines. Thus,it is essential to monitor the operational status effectively and precisely. The operating condition characteristics, main failure modes and failure mechanisms of the main-shaft bearings were sorted out. Existing main-shaft bearing monitoring technologies were summarized and compared in terms of vibration, lubricating status, sound, acoustic emission,and temperature. The method and technical characteristics of main-shaft bearings condition monitoring based on multi-sensor information fusion were discussed. Result showed that,the influences of the material and structural characteristics on output signals,the micro and wireless sensors,efficient multi-sensor information fusion methods,and data interaction between the physical and the digital model would become the future research direction of main-shaft bearings.
作者 刘朋 王黎钦 张传伟 郑德志 LIU Peng;WANG Liqin;ZHANG Chuanwei;ZHENG Dezhi(Ministry of Industry and Information Technology Key Laboratory of Aerospace Bearing Technology and Equipment,Harbin Institute of Technology,Harbin 150001,China;State Key Laboratory of Robotics and System,Harbin Institute of Technology,Harbin 150001,China)
出处 《航空动力学报》 EI CAS CSCD 北大核心 2022年第2期330-343,共14页 Journal of Aerospace Power
基金 国家重点研发计划(2018YFB0703804)。
关键词 航空发动机主轴轴承 失效模式 状态监测 多传感器 信息融合 aircraft engines main-shaft bearings failure mode condition monitoring multi-sensor information fusion
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