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基于磨损区域静电监测的滚动轴承故障信号特征分析

333    2024-05-24

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作者:顾双双1, 刘若晨1, 严旭1, 孙见忠2, 贝绍轶1

作者单位:1. 江苏理工学院汽车与交通工程学院,江苏 常州 213001;
2. 南京航空航天大学民航学院,江苏 南京 211106


关键词:静电监测;滚动轴承;磨损区域;故障特征;信号分析


摘要:

针对滚动轴承振动监测信号耦合多部件激励干扰问题,引入静电监测技术,对轴承磨损区域的荷电水平进行监测研究。设计并搭建滚动轴承静电监测实验平台,通过不同转速下的故障模拟实验,完成滚动轴承静电监测;对多组监测信号进行时频域分析,并与振动监测结果进行比较。实验结果表明,静电信号在各不同转速下均能监测到各对应故障位置的特征频率,且与理论特征值相匹配;实测同组静电信号与振动信号相比,静电信号所含干扰较小,故障特征信号更为明显。研究方法和结果证明静电监测技术可避免多激励源,对滚动轴承磨损区域监测具备优势。


Feature analysis of rolling bearing fault signal based on electrostatic monitoring in wear site
GU Shuangshuang1, LIU Ruochen1, YAN Xu1, SUN Jianzhong2, BEI Shaoyi1
1. School of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou 213001, China;
2. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Abstract: Aiming at the problem of coupling multi-component excitation interference of rolling bearing vibration monitoring signals, the electrostatic monitoring technology is introduced to monitor the charge level in the bearing wear site. An experimental platform for electrostatic monitoring of rolling bearing was designed and built, and electrostatic monitoring of rolling bearing was completed through fault simulation experiments at different rotating speeds. Several monitoring signals were analyzed in time-frequency domain and compared with vibration monitoring results. The experimental results show that the characteristic frequencies of corresponding fault locations can be monitored by electrostatic signals at different rotating speeds and matched with theoretical characteristic values. Compared with vibration signals, the interference of electrostatic signals is smaller and the fault characteristic signals are more obvious. The research method and results show that the electrostatic monitoring technology avoids multiple excitation sources and has advantages in monitoring the wear site of rolling bearing.
Keywords: electrostatic monitoring;rolling bearing;wear site;fault characteristics;signal analysis
2024, 50(5):145-152  收稿日期: 2021-12-23;收到修改稿日期: 2022-04-30
基金项目: 国家自然科学基金(51705221,91860139,52072176);江苏理工学院研究生实践创新计划项目(XSJCX22_40)
作者简介: 顾双双(1997-),男,江苏丹阳市人,硕士研究生,专业方向为故障诊断、信号处理。
参考文献
[1] 赵磊, 张永祥, 朱丹宸. 复杂装备滚动轴承的故障诊断与预测方法研究综述[J]. 中国测试, 2020, 46(3): 17-25
ZHAO L, ZHANG Y X, ZHU D C. Review on rolling bearing fault diagnosis and prognostic for complex equipment[J]. China Measurement & Test, 2020, 46(3): 17-25
[2] 刘鹏鹏, 左洪福, 孙见忠, 等. 气路静电监测技术在涡喷发动机试车中的应用[J]. 中国机械工程, 2013, 24(20): 2758-2763
LIU P P, ZUO H F, SUN J Z, et al. Research on gas path electrostatic monitoring technology being used in a new turbojet engine[J]. China Mechanical Engineering, 2013, 24(20): 2758-2763
[3] 刘若晨, 左洪福, 张营, 等. 滚动轴承性能退化静电监测方法及试验[J]. 机械工程学报, 2014, 50(23): 75-81
LIU R C, ZUO H F, ZHANG Y, et al. Electrostatic monitoring of oil lubricated rolling bearing performance degradation and experiment[J]. Journal of Mechanical Engineering, 2014, 50(23): 75-81
[4] WANG L, WOOD R J K, CARE I, et al. Electrostatic wear sensing of ceramic-steel lubricated contact[J]. Tribology Series, 2003, 43: 711-720
[5] CHEN S L, WOOD R J K, WANG L, et al. Wear detection of rolling element bearings using multiple-sensing technologies and mixture-model-based clustering method[J]. Proceedings of the Institution of Mechanical Engineers, Part O:Journal of Risk and Reliability, 2008, 222(2): 207-208
[6] CRAIG M, HARVEY T J, WOOD R J K, et al. Advanced condition monitoring of tapered roller bearings, Part 1[J]. Tribology International, 2009, 42(11): 1846-1856
[7] HARVEY T J, WOOD R J K, POWRIE H E G. Electrostatic wear monitoring of rolling element bearings[J]. Wear, 2007, 263(7): 1492-1501
[8] 陈志雄, 左洪福, 詹志娟, 等. 黄铜全流量在线磨粒静电监测实验研究[J]. 中国机械工程, 2012, 23(15): 1848-1854
CHEN Z X, ZUO H F, ZHAN Z J, et al. Experiment study for oil--line debris electrostatic monitoring of brass[J]. China Mechanical Engineering, 2012, 23(15): 1848-1854
[9] 佟佩声. 滚动轴承变工况条件下静电监测特征提取及故障程度识别方法研究[D]. 南京: 南京航空航天大学, 2014.
TONG P S. Electrostatic monitoring feature extraction and fault severity assessment methods for rolling bearing based on variable operating condition[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014.
[10] 刘若晨, 左洪福, 孙见忠, 等. 车辆齿轮箱静电监测[J]. 交通运输工程学报, 2015, 15(1): 50-57+73
LIU R C, ZUO H F, SUN J Z, et al. Electrostatic monitoring of vehicle gearbox[J]. Journal of Traffic and Transportation Engineering, 2015, 15(1): 50-57+73
[11] 刘若晨, 徐成, 左洪福, 等. 变工况下滚动轴承静电多传感器融合监测方法研究[J]. 机械设计与制造, 2021(2): 40-44
LIU R C, XU C, ZUO H F, et al. Research on the method of electrostatic multi-sensor fusion monitoring of rolling bearing under variable working conditions[J]. Machinery Design & Manufacture, 2021(2): 40-44
[12] LIU R C, ZUO H F, SUN J Z, et al. Electrostatic monitoring of wind turbine gearbox on oil-lubricated system[J]. Proceedings of the Institution of Mechanical Engineers, Part C:Journal of Mechanical Engineering Science, 2017, 231(19): 3649-3664
[13] ZHANG Y, ZUO H F, BAI F. Feature extraction for rolling bearing fault diagnosis by electrostatic monitoring sensors[J]. Proceedings of the Institution of Mechanical Engineers, Part C:Journal of Mechanical Engineering Science, 2015, 229(10): 1887-1903
[14] ZHANG Y, WANG A C, ZUO H F. Roller bearing performance degradation assessment based on fusion of multiple features of electrostatic sensors[J]. Sensors (Basel, Switzerland), 2019, 19(4): 824-838
[15] 钟勇, 李三雁, 荣本阳, 等. 基于振动信号排列熵和集成支持向量机的滚动轴承退化状态评估[J]. 中国测试, 2021, 47(7): 13-18
ZHOGN Y, LI S Y, RONG B Y, et al. Degradation status assessment for rolling element bearings based on vibration signal permutation entropy and ensemble support vector machine[J]. China Measurement & Test, 2021, 47(7): 13-18