留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于短时傅里叶变换及其倒频谱的滚动轴承静电监测

刘若晨 顾双双 孙见忠 左洪福 贝绍轶

刘若晨, 顾双双, 孙见忠, 等. 基于短时傅里叶变换及其倒频谱的滚动轴承静电监测[J]. 航空动力学报, 2024, 39(9):20220699 doi: 10.13224/j.cnki.jasp.20220699
引用本文: 刘若晨, 顾双双, 孙见忠, 等. 基于短时傅里叶变换及其倒频谱的滚动轴承静电监测[J]. 航空动力学报, 2024, 39(9):20220699 doi: 10.13224/j.cnki.jasp.20220699
LIU Ruochen, GU Shuangshuang, SUN Jianzhong, et al. Electrostatic monitoring of rolling bearings based on short-time Fourier transform and cepstrum[J]. Journal of Aerospace Power, 2024, 39(9):20220699 doi: 10.13224/j.cnki.jasp.20220699
Citation: LIU Ruochen, GU Shuangshuang, SUN Jianzhong, et al. Electrostatic monitoring of rolling bearings based on short-time Fourier transform and cepstrum[J]. Journal of Aerospace Power, 2024, 39(9):20220699 doi: 10.13224/j.cnki.jasp.20220699

基于短时傅里叶变换及其倒频谱的滚动轴承静电监测

doi: 10.13224/j.cnki.jasp.20220699
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(XSJCX20_44);“江苏省政府留学奖学金”资助项目
详细信息
    作者简介:

    刘若晨(1989-),男,副教授,博士,主要从事智能传感技术、寿命预测与健康管理研究

  • 中图分类号: V216.3;TH133.33

Electrostatic monitoring of rolling bearings based on short-time Fourier transform and cepstrum

  • 摘要:

    针对滚动轴承在常规监测方法下耦合多激励源问题,引入静电监测技术,提出了基于短时傅里叶变换及其倒频谱的故障特征提取方法。设计搭建了滚动轴承静电监测试验平台,采集正常和故障情况下滚动轴承的静电、振动信号。从静电信号的时域、频域和时频域角度对比研究,证明了使用时频分析联合倒频谱的方法能准确提取出与实际轴承故障位置相匹配的特征值;对比同步振动信号的故障特征,展现了静电信号低频特征突出、高频衰减快的特点。试验结果表明:滚动轴承发生早期磨损故障后会伴随强烈的静电现象产生。对静电信号进行短时傅里叶变换及倒频谱分析,能有效去除高频激励源,凸显出低频段内轴承故障特征。相比于振动检测,静电检测采集的信号源能较为直接地反映轴承故障信息,为设备故障诊断提供了一种思路。

     

  • 图 1  静电监测原理

    Figure 1.  Principle of electrostatic monitoring

    图 2  STFT原理

    Figure 2.  STFT principle

    图 3  磨损区域静电传感器

    Figure 3.  Wear area electrostatic sensors

    图 4  滚动轴承试验件

    Figure 4.  Rolling bearing test pieces

    图 5  滚动轴承试验监测平台及原理

    Figure 5.  Rolling bearing test monitoring platform and principle

    图 6  正常、故障轴承静电信号时域波形图

    Figure 6.  Time domain waveforms of normal and faulty bearing electrostatic signals

    图 7  正常轴承静电监测结果

    Figure 7.  Electrostatic monitoring results of normal bearing

    图 8  故障轴承静电监测结果

    Figure 8.  Electrostatic monitoring results of faulty bearing

    图 9  滚动轴承静电、振动信号时域波形图

    Figure 9.  Time domain waveforms of rolling bearing electrostatic and vibration signals

    图 10  故障轴承振动监测结果

    Figure 10.  Vibration monitoring results of faulty bearing

    表  1  滚动轴承主要参数

    Table  1.   Main parameters of rolling bearings

    轴承
    型号
    节径
    D/mm
    滚子
    直径d/mm
    滚子数
    Z
    接触角
    $\varphi $/(°)
    6204-2Z 33.5 7.94 8 0
    下载: 导出CSV
  • [1] 康伟,朱永生,闫柯,等. 基于CSES和MED的滚动轴承微弱故障特征提取[J]. 振动、测试与诊断,2021,41(4): 660-666,827. KANG Wei,ZHU Yongsheng,YAN Ke,et al. Weak fault extraction of rolling element bearings based on CSES and MED[J]. Journal of Vibration,Measurement & Diagnosis,2021,41(4): 660-666,827. (in Chinese

    KANG Wei, ZHU Yongsheng, YAN Ke, et al. Weak fault extraction of rolling element bearings based on CSES and MED[J]. Journal of Vibration, Measurement & Diagnosis, 2021, 41(4): 660-666, 827. (in Chinese)
    [2] CHEN S L,WOOD R 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): 207-218.
    [3] 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(11/12): 1846-1856.
    [4] 张营,左洪福,佟佩声,等. 基于谱插值和奇异值差分谱的滚动轴承静电监测信号去噪方法[J]. 航空动力学报,2014,29(8): 1996-2002. ZHANG Ying,ZUO Hongfu,TONG Peisheng,et al. Denoising method for electrostatic monitoring signal of roller bearing based on spectrum interpolation and difference spectrum of singular value[J]. Journal of Aerospace Power,2014,29(8): 1996-2002. (in Chinese

    ZHANG Ying, ZUO Hongfu, TONG Peisheng, et al. Denoising method for electrostatic monitoring signal of roller bearing based on spectrum interpolation and difference spectrum of singular value[J]. Journal of Aerospace Power, 2014, 29(8): 1996-2002. (in Chinese)
    [5] ZHANG Ying,WANG Anchen,ZUO Hongfu. Roller bearing performance degradation assessment based on fusion of multiple features of electrostatic sensors[J]. Sensors,2019,19(4): 824. doi: 10.3390/s19040824
    [6] 文振华,侯军兴,左洪福. 航空发动机静电监测信号的特征分析及提取[J]. 振动、测试与诊断,2015,35(3): 453-458,588. WEN Zhenhua,HOU Junxing,ZUO Hongfu. Characteristics analysis and extraction method for electrostatic monitoring signal in aero-engines gas path[J]. Journal of Vibration,Measurement & Diagnosis,2015,35(3): 453-458,588. (in Chinese

    WEN Zhenhua, HOU Junxing, ZUO Hongfu. Characteristics analysis and extraction method for electrostatic monitoring signal in aero-engines gas path[J]. Journal of Vibration, Measurement & Diagnosis, 2015, 35(3): 453-458, 588. (in Chinese)
    [7] 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 Ruochen,XU Cheng,ZUO Hongfu,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. (in Chinese

    LIU Ruochen, XU Cheng, ZUO Hongfu, 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. (in Chinese)
    [8] LIU Ruochen,WANG Han,ZHANG Jinwu,et al. Research on electrostatic monitoring of tribo-contacts with dynamic adaptive fusion method[J]. Mathematical Problems in Engineering,2022,2022: 1-14.
    [9] LIU Ruochen,ZUO Hongfu,SUN Jianzhong,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.
    [10] 李婷,付德义,薛扬. 基于AE与STFT的变桨轴承裂纹诊断研究[J]. 振动、测试与诊断,2021,41(2): 299-303,412. LI Ting,FU Deyi,XUE Yang. Research on crack diagnosis of pitch bearing based on AE and STFT[J]. Journal of Vibration,Measurement & Diagnosis,2021,41(2): 299-303,412. (in Chinese

    LI Ting, FU Deyi, XUE Yang. Research on crack diagnosis of pitch bearing based on AE and STFT[J]. Journal of Vibration, Measurement & Diagnosis, 2021, 41(2): 299-303, 412. (in Chinese)
    [11] 段晨东,高鹏,徐先峰,等. 一种基于时频峭度谱的滚动轴承损伤诊断方法[J]. 机械工程学报,2015,51(11): 78-83. DUAN Chendong,GAO Peng,XU Xianfeng,et al. A ball bearing defect diagnosis method using time-frequency kurtosis spectrum[J]. Journal of Mechanical Engineering,2015,51(11): 78-83. (in Chinese doi: 10.3901/JME.2015.11.078

    DUAN Chendong, GAO Peng, XU Xianfeng, et al. A ball bearing defect diagnosis method using time-frequency kurtosis spectrum[J]. Journal of Mechanical Engineering, 2015, 51(11): 78-83. (in Chinese) doi: 10.3901/JME.2015.11.078
    [12] 陈剑,杨斌,黄凯旋,等. 一种脊线提取方法在轴承故障诊断中的应用[J]. 中国机械工程,2021,32(10): 1157-1163. CHEN Jian,YANG Bin,HUANG Kaixuan,et al. Applications of a ridgeline extraction method in bearing fault diagnosis[J]. China Mechanical Engineering,2021,32(10): 1157-1163. (in Chinese

    CHEN Jian, YANG Bin, HUANG Kaixuan, et al. Applications of a ridgeline extraction method in bearing fault diagnosis[J]. China Mechanical Engineering, 2021, 32(10): 1157-1163. (in Chinese)
    [13] 姜衡,左洪福,郭家琛,等. 航空发动机叶尖径向间隙静电监测方法[J]. 航空动力学报,2021,36(3): 466-476. JIANG Heng,ZUO Hongfu,GUO Jiachen,et al. Electrostatic monitoring method for blade tip radial clearance of aero-engine[J]. Journal of Aerospace Power,2021,36(3): 466-476. (in Chinese

    JIANG Heng, ZUO Hongfu, GUO Jiachen, et al. Electrostatic monitoring method for blade tip radial clearance of aero-engine[J]. Journal of Aerospace Power, 2021, 36(3): 466-476. (in Chinese)
    [14] 黄建明,瞿合祚,李晓明. 基于短时傅里叶变换及其谱峭度的电能质量混合扰动分类[J]. 电网技术,2016,40(10): 3184-3191. HUANG Jianming,QU Hezuo,LI Xiaoming. Classification for hybrid power quality disturbance based on STFT and its spectral kurtosis[J]. Power System Technology,2016,40(10): 3184-3191. (in Chinese

    HUANG Jianming, QU Hezuo, LI Xiaoming. Classification for hybrid power quality disturbance based on STFT and its spectral kurtosis[J]. Power System Technology, 2016, 40(10): 3184-3191. (in Chinese)
    [15] 高云鹏,滕召胜,温和,等. 凯塞窗插值FFT的电力谐波分析与应用[J]. 中国电机工程学报,2010,30(4): 43-48. GAO Yunpeng,TENG Zhaosheng,WEN He,et al. Harmonic analysis based on Kaiser window interpolation FFT and its application[J]. Proceedings of the CSEE,2010,30(4): 43-48. (in Chinese

    GAO Yunpeng, TENG Zhaosheng, WEN He, et al. Harmonic analysis based on Kaiser window interpolation FFT and its application[J]. Proceedings of the CSEE, 2010, 30(4): 43-48. (in Chinese)
    [16] 罗毅,甄立敬. 基于小波包与倒频谱分析的风电机组齿轮箱齿轮裂纹诊断方法[J]. 振动与冲击,2015,34(3): 210-214. LUO Yi,ZHEN Lijing. Diagnosis method of turbine gearbox gearcrack based on wavelet packet and cepstrum analysis[J]. Journal of Vibration and Shock,2015,34(3): 210-214. (in Chinese

    LUO Yi, ZHEN Lijing. Diagnosis method of turbine gearbox gearcrack based on wavelet packet and cepstrum analysis[J]. Journal of Vibration and Shock, 2015, 34(3): 210-214. (in Chinese)
    [17] 孙伟,李新民,金小强,等. 应用EMD和倒包络谱分析的故障提取方法[J]. 振动、测试与诊断,2018,38(5): 1057-1062,1087. SUN Wei,LI Xinmin,JIN Xiaoqiang,et al. Feature extraction method based on MED and envelope cepstrum[J]. Journal of Vibration,Measurement & Diagnosis,2018,38(5): 1057-1062,1087. (in Chinese

    SUN Wei, LI Xinmin, JIN Xiaoqiang, et al. Feature extraction method based on MED and envelope cepstrum[J]. Journal of Vibration, Measurement & Diagnosis, 2018, 38(5): 1057-1062, 1087. (in Chinese)
    [18] 李昊泽,贺雅,冯坤,等. 考虑时变激励的滚动轴承局部故障动力学建模[J]. 航空学报,2022,43(8): 625176. LI Haoze,HE Ya,FENG Kun,et al. Dynamic modeling of rolling bearing local fault considering time-varying excitation[J]. Acta Aeronautica et Astronautica Sinica,2022,43(8): 625176. (in Chinese

    LI Haoze, HE Ya, FENG Kun, et al. Dynamic modeling of rolling bearing local fault considering time-varying excitation[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(8): 625176. (in Chinese)
    [19] 陈仁祥,朱玉清,胡小林,等. 自适应正则化迁移学习的不同工况下滚动轴承故障诊断[J]. 仪器仪表学报,2021,41(8): 95-103. CHEN Renxiang,ZHU Yuqing,HU Xiaolin,et al. Fault diagnosis of rolling bearing under different working conditions using adaptation regularization based transfer learning[J]. Chinese Journal of Scientific Instrument,2021,41(8): 95-103. (in Chinese

    CHEN Renxiang, ZHU Yuqing, HU Xiaolin, et al. Fault diagnosis of rolling bearing under different working conditions using adaptation regularization based transfer learning[J]. Chinese Journal of Scientific Instrument, 2021, 41(8): 95-103. (in Chinese)
  • 加载中
图(10) / 表(1)
计量
  • 文章访问数:  135
  • HTML浏览量:  96
  • PDF量:  29
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-09-17
  • 网络出版日期:  2023-12-21

目录

    /

    返回文章
    返回