流体动压轴承状态监测与故障诊断研究进展  

Research Progress in Condition Monitoring and Fault Diagnosis of Hydrodynamic Bearing

作　　者：刘顺顺 卢绪祥[1] 刘瑞[1] LIU Shunshun;LU Xuxiang;LIU Rui(School of Energy and Power Engineering,Changsha University of Science and Technology,Changsha 410114,Hunan,China)

机构地区：[1]长沙理工大学能源与动力工程学院,湖南长沙410114

出　　处：《汕头大学学报（自然科学版）》2023年第1期35-47,共13页Journal of Shantou University：Natural Science Edition

基　　金：湖南省普通高校创新平台开放基金资助项目(16K002)。

摘　　要：滑动轴承是大型旋转机械中的重要支撑设备,对滑动轴承进行润滑状态监测及早期故障诊断对机组安全生产有重要意义.本文阐述了滑动轴承的故障状态及失效形式,详细说明了振动信号、声发射信号及铁谱分析等技术在滑动轴承状态监测中的运用,综述了专家系统、神经网络和支持向量机在滑动轴承故障识别中的有效应用.通过分析对未来的发展趋势进行了展望:结合新一代人工智能与传感技术,通过多物理场信号进行融合监测,发展建立轴承信号标准数据库,结合可视化研究和远程监测,实现快速精准的滑动轴承状态监测与故障诊断.Sliding bearings are important supporting equipment in large rotating machinery.The lubrication state monitoring and early fault diagnosis of sliding bearings are of great significance to the safe production of units.In this paper,the failure state and failure mode of sliding bearing are expounded,the application of vibration signal,acoustic emission signal and ferrography analysis and other technologies in the monitoring of the sliding bearing condition is detailed,and the expert system,neural network and support vector machine technology for effective application in fault identification of plain bearings are summarized.Through the analysis,the future development trend is prospected:combining a new generation of artificial intelligence and sensing technology,integrating monitoring through multi-physical field signals,developing and establishing a standard database of bearing signals,and combining visual research and remote monitoring to achieve accurate and high-speed sliding bearings condition monitoring and fault diagnosis.

关 键 词：滑动轴承 状态监测 故障诊断 神经网络 人工智能 

分 类 号：TH133.31[机械工程—机械制造及自动化] TH165.3