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GWO-NLM与CEEMDAN的滚动轴承故障诊断方法

栾孝驰 徐石 沙云东 柳贡民 唐金宇 张席 李壮

栾孝驰, 徐石, 沙云东, 等. 基于GWO-NLM与CEEMDAN的滚动轴承故障诊断方法[J]. 航空动力学报, 2023, 38(5):1185-1197 doi: 10.13224/j.cnki.jasp.20210547
引用本文: 栾孝驰, 徐石, 沙云东, 等. 基于GWO-NLM与CEEMDAN的滚动轴承故障诊断方法[J]. 航空动力学报, 2023, 38(5):1185-1197 doi: 10.13224/j.cnki.jasp.20210547
LUAN Xiaochi, XU Shi, SHA Yundong, et al. Rolling bearing fault diagnosis method based on GWO-NLM and CEEMDAN[J]. Journal of Aerospace Power, 2023, 38(5):1185-1197 doi: 10.13224/j.cnki.jasp.20210547
Citation: LUAN Xiaochi, XU Shi, SHA Yundong, et al. Rolling bearing fault diagnosis method based on GWO-NLM and CEEMDAN[J]. Journal of Aerospace Power, 2023, 38(5):1185-1197 doi: 10.13224/j.cnki.jasp.20210547

基于GWO-NLM与CEEMDAN的滚动轴承故障诊断方法

doi: 10.13224/j.cnki.jasp.20210547
基金项目: 辽宁省教育厅系列项目(JYT2020010); 2021年辽宁省大学生创新创业训练计划(S202110143021);中国航发产学研合作项目(HFZL2018CXY017)
详细信息
    作者简介:

    栾孝驰(1987-),男,副教授、硕士生导师,博士,主要从事航空发动机轴承、齿轮传动系统动力学分析及故障诊断研究。E-mail:luanxiaochi27@163.com

  • 中图分类号: V263.6

Rolling bearing fault diagnosis method based on GWO-NLM and CEEMDAN

  • 摘要:

    针对滚动轴承故障振动信号受背景噪声干扰大、故障特征不易提取的问题,提出了基于灰狼算法(GWO)优化的非局部均值去噪(NLM)和完全自适应噪声集合经验模态分解(CEEMDAN)相结合的轴承故障诊断方法。先将CEEMDAN和相关系数-能量比-峭度准则作为预处理手段,并进行信号重构;然后使用灰狼算法对NLM的参数进行优化,利用最优参数对重构信号进行降噪,将降噪后的信号通过SG(Savitzky-Golay)滤波进行二次降噪,得到最终去噪信号,对最终信号进行包络分析得到诊断结果。GWO-NLM去噪、CEEMDAN和包络分析的混合特征提取技术,由仿真信号可知去噪后的信噪比提高了9.31 dB,由实验信号可知能清晰地提取轴承的故障特征频率及倍频、转频以及故障特征频率与转频的系列调制频率。

     

  • 图 1  GWO-NLM的轴承故障诊断模型

    Figure 1.  Bearing fault diagnosis model of GWO-NLM

    图 2  仿真原始信号

    Figure 2.  Simulation of the original signal

    图 3  加噪后仿真信号

    Figure 3.  Simulated signal after noise addition

    图 4  降噪后信号时域波形

    Figure 4.  Time domain waveform of the signal after noise reduction

    图 5  降噪后信号部分频段包络谱

    Figure 5.  Partial frequency band envelope spectrum of the signal after noise reduction

    图 6  滚动轴承故障模拟实验台

    Figure 6.  Rolling bearing fault simulation experiment rig

    图 7  内圈故障时域波形

    Figure 7.  Time domain waveform of inner ring fault

    图 8  内圈故障信号频谱图

    Figure 8.  Spectrogram of inner ring fault signal

    图 9  重构后内圈故障时域波形

    Figure 9.  Time domain waveform of inner ring fault after reconstruction

    图 10  经GWO-NLM-SG滤波后信号(内圈故障)

    Figure 10.  Signal after filtering by GWO-NLM-SG (inner circle fault)

    图 11  经GWO-NLM-SG滤波后信号包络谱(内圈故障)

    Figure 11.  Envelope spectrum of the signal after filtering by GWO-NLM-SG (inner ring fault)

    图 12  经GWO-NLM-SG滤波后信号部分包络谱(内圈故障)

    Figure 12.  Partial envelope spectrum of the signal after filtering by GWO-NLM-SG (inner ring fault)

    图 13  外圈故障信号的时域波形

    Figure 13.  Time domain waveform of outer ring fault signal

    图 14  外圈故障信号的频谱图

    Figure 14.  Spectrogram of outer ring fault signal

    图 15  经GWO-NLM-SG滤波后的信号(外圈故障)

    Figure 15.  Signal after filtering by GWO-NLM-SG (outer ring fault)

    图 16  经GWO-NLM-SG滤波后的信号包络谱 (外圈故障)

    Figure 16.  Envelope spectrum of the signal after GWO-NLM-SG filtering (outer ring fault)

    图 17  经GWO-NLM-SG滤波后的信号部分包络谱(外圈故障)

    Figure 17.  Partial envelope spectrum of the signal after GWO-NLM-SG filtering (outer ring fault)

    图 18  滚棒轴承故障模拟实验台

    Figure 18.  Experiment rig of fault simulation of the roller bearing

    图 19  线切割加工的不同故障滚棒轴承照片

    Figure 19.  Different fault photos of the roller bearings with wire cutting defects

    图 20  滚棒轴承外圈故障去噪信号包络谱

    Figure 20.  Envelope spectrum of the denoised signal of the outer ring of the roller bearing

    图 21  滚棒轴承滚动体故障去噪信号包络谱

    Figure 21.  Envelope spectrum of the roller bearing rolling element fault denoising signal

    表  1  不同信号信噪比

    Table  1.   Signal-to-noise ratio of different signals

    信号信噪比/dB
    原始信号−5.47
    NLM0.638
    GWO-NLM3.51
    GWO-NLM-SG3.84
    下载: 导出CSV

    表  2  6205-2RS JEM SKF深沟球轴承几何参数

    Table  2.   6205-2RS JEM SKF deep groove ball bearing geometric parameters

    参数数值
    节径 D/mm39.039
    滚珠直径 d/mm7.940
    接触角 $ \alpha $/(°)0
    滚珠数 Z9
    下载: 导出CSV

    表  3  内圈故障数据实验参数

    Table  3.   Experimental parameters for inner ring fault data

    参数数值
    转速/(r/min)1797
    故障直径/mm0.18
    故障深度/mm0.28
    负载/W0
    下载: 导出CSV

    表  4  外圈故障数据实验参数

    Table  4.   Experimental parameters for outer ring fault data

    参数数值
    转速/(r/min)1797
    故障直径/mm0.18
    故障深度/mm0.28
    下载: 导出CSV

    表  5  前6个IMFs的能量比、相关系数、峭度

    Table  5.   Energy ratio, correlation coefficient, kurtosis of the first six IMFs

    IMF能量比相关系数峭度$K_ { {\rm{r} }\text{ε} }$值
    11.000.860.150.67
    20.020.441.000.48
    30.070.450.250.25
    40.050.350.030.14
    50.060.230.020.106
    60.050.100.010.04
    下载: 导出CSV

    表  6  转频及其倍频、故障频率及其倍频、调制频率(部分包络谱、外圈故障)

    Table  6.   Rotation frequency and its multiplier, fault frequency and its multiplier, modulation frequency (partial envelope spectrum, outer ring fault)

    特征频率n
    1234567
    $n{F_{\text{r}}}$
    $n{f_{\rm{o}}}$
    ${f_{\rm{o}}} - n{F_{\rm{r}}}$
    ${f_{\rm{o}}} + n{F_{\rm{r}}}$
    $2{f_{\rm{o}}} - n{F_{\rm{r}}}$
    $2{f_{\rm{o}}} + n{F_{\rm{r}}}$
    $3{f_{\rm{o}}} - n{F_{\rm{r}}}$
    $3{f_{\rm{o}}} + n{F_{\rm{r}}}$
    注:n为频率倍数;√代表可以提取出该特征频率。
    下载: 导出CSV

    表  7  NJ204EM滚棒轴承参数

    Table  7.   Parameters of NJ204EM roller bearing

    参数数值
    节径D/mm33.5
    滚棒直径d/mm7
    接触 α/(°)0
    滚棒数Z11
    下载: 导出CSV
  • [1] 武英杰,辛红伟,王建国,等. 基于VMD滤波和极值点包络阶次的滚动轴承故障诊断[J]. 振动与冲击,2018,37(14): 102-107.

    WU Yingjie,XIN Hongwei,WANG Jianguo,et al. Rolling bearing fault diagnosis based on the variational mode decomposition filtering and extreme point envelope order[J]. Journal of Vibration and Shock,2018,37(14): 102-107. (in Chinese)
    [2] 邓飞跃,强亚文,杨绍普,等. 一种自适应频率窗经验小波变换的滚动轴承故障诊断方法[J]. 西安交通大学学报,2018,52(8): 22-29.

    DENG Feiyue,QIANG Yawen,YANG Shaopu,et al. A fault diagnosis method of rolling element bearings with adaptive frequency window empirical wavelet transform[J]. Journal of Xi’an Jiaotong University,2018,52(8): 22-29. (in Chinese)
    [3] 陶洁, 刘义伦, 付卓, 等. 基于Teager能量算子和深度置信网络的滚动轴承故障诊断[J]. 中南大学学报(自然科学版), 2017, 48(1): 61-68.

    TAO Jie, LIU Yilun, FU Zhuo, et al. Fault damage degrees diagnosis for rolling bearing based on Teager energy operator and deep belief network[J]. Journal of Central South University (Science and Technology), 2017, 48(1): 61-68. (in Chinese)
    [4] YAN Xiaoan,JIA Minping. Application of CSA-VMD and optimal scale morphological slice bispectrum in enhancing outer race fault detection of rolling element bearings[J]. Mechanical Systems and Signal Processing,2019,122: 56-86. doi: 10.1016/j.ymssp.2018.12.022
    [5] AI Yanting,GUAN Jiaoyue,FEI Chengwei,et al. Fusion information entropy method of rolling bearing fault diagnosis based on n-dimensional characteristic parameter distance[J]. Mechanical Systems and Signal Processing,2017,88: 123-136. doi: 10.1016/j.ymssp.2016.11.019
    [6] 陈鹏, 赵小强. 基于优化VMD与改进阈值降噪的滚动轴承早期故障特征提取[J]. 振动与冲击, 2021, 40(13): 146-153.

    CHEN Peng, ZHAO Xiaoqiang. Early fault feature extraction of rolling bearing based on optimized VMD and improved threshold denoising[J]. Journal of Vibration and Shock, 2021, 40(13): 146-153. (in Chinese)
    [7] 张景超, 张金敏, 张淑清, 等 . 基于小波及非线性预测的轴承故障诊断方法[J]. 仪器仪表学报, 2012, 33(1): 127-131.

    ZHANG Jingchao, ZHANG Jinmin, ZHANG Shuqing, et al. Bearing fault diagnosis method based on wavelet analysis and nonlinear prediction[J]. Chinese Journal of Scientific Instrument, 2012, 33(1): 127-131. (in Chinese)
    [8] NORDEN H E. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society: A Mathematical, Physical and Engineering Sciences,1998,454(1971): 903-995. doi: 10.1098/rspa.1998.0193
    [9] WU Zhaohua,HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis,2009,1(1): 1-41. doi: 10.1142/S1793536909000047
    [10] 李军,李青. 基于CEEMDAN-排列熵和泄漏积分ESN的中期电力负荷预测研究[J]. 电机与控制学报,2015,19(8): 70-80.

    LI Jun,LI Qing. Medium term electricity load forecasting based on CEEMDAN-permutation entropy and ESN with leaky integrator neurons[J]. Electric Machines and Control,2015,19(8): 70-80. (in Chinese)
    [11] 任学平, 王朝阁, 张玉皓, 等. 基于DT-CWT自适应Teager能量谱的轴承早期故障诊断[J]. 振动、测试与诊断, 2017, 37(4): 735-742, 842.

    REN Xueping, WANG Chaoge, ZHANG Yuhao, et al. Early fault diagnosis of rolling bearing based on dual-tree complex wavelet transform adaptive teager energy spectrum[J]. Journal of Vibration, Measurement and Diagnosis, 2017, 37(4): 735-742, 842. (in Chinese)
    [12] 赵洪山,李浪,王颖. 一种基于盲源分离和流形学习的风电机组轴承故障特征提取方法[J]. 太阳能学报,2016,37(2): 269-275.

    ZHAO Hongshan,LI Lang,WANG Ying. Fault feature extraction method of wind turbine bearing based on blind source separation and manifold learning[J]. Acta Energiae Solaris Sinica,2016,37(2): 269-275. (in Chinese)
    [13] BUADES A,COLL B,MOREL J M. A review of image denoising algorithms, with a new one[J]. Multiscale Modeling and Simulation,2005,4(2): 490-530. doi: 10.1137/040616024
    [14] 胡新海, 欧阳永林, 曾庆才, 等. 叠前非局部平均滤波压制随机噪音[J]. 煤田地质与勘探, 2014, 42(5): 87-91.

    HU Xinhai, OUYANG Yonglin, ZHNG Qingcai, et al. De-noising seismic data with pre-stack nonlocal means method[J]. Coal Geology and Exploration, 2014, 42(5): 87-91. (in Chinese)
    [15] TRACEY B H,MILLER E L. Nonlocal means denoising of ECG signals.[J]. IEEE Transactions on Bio-Medical Engineering,2012,59(9): 2383-2386. doi: 10.1109/TBME.2012.2208964
    [16] LÜ Yong,ZHU Qinglin,YUAN Rui. Fault diagnosis of rolling bearing based on fast nonlocal means and envelop spectrum[J]. Sensors,2015,15(1): 1182-1198. doi: 10.3390/s150101182
    [17] 熊国良,胡俊锋,陈慧,等. 基于SK-NLM包络的滚动轴承故障冲击特征增强[J]. 仪器仪表学报,2016,37(10): 2176-2184.

    XIONG Guoliang,HU Junfeng,CHEN Hui,et al. Rolling bearing fault impact feature enhancement based on spectral kurtosis and non-local means (SK-NLM)[J]. Chinese Journal of Scientific Instrument,2016,37(10): 2176-2184. (in Chinese)
    [18] KUAI Moshen,CHENG Guang,PANG Yusong,et al. Research of planetary gear fault diagnosis based on permutation entropy of CEEMDAN and ANFIS[J]. Sensors,2018,18(3): 782.1-782.17. doi: 10.3390/s18030782
    [19] 谷然, 陈捷, 洪荣晶, 等. 基于改进自适应变分模态分解的滚动轴承微弱故障诊断[J]. 振动与冲击, 2020, 39(8): 1-7, 22

    GU Ran, CHEN Jie, HONG Rongjing, et al. Early fault diagnosis of rolling bearings based on adaptive variational mode decomposition and the Teager energy operator[J]. Journal of Vibration and Shock, 2020, 39(8): 1-7, 22. (in Chinese)
    [20] 唐晓红, 胡俊锋, 熊国良, 等. 自适应非局部均值及在轴承故障检测中的应用[J]. 振动. 测试与诊断, 2019, 39(1): 61-67, 221.

    TANG Xiaohong, HU Junfeng, XIONG Guoliang, et al. Adaptive non-local means with applications in fault detection of rolling[J]. Journal of Vibration, Measurement and Diagnosis, 2019, 39(1): 61-67, 221. (in Chinese)
    [21] 陆子鸣. 基于NLM-VMD和度量学习的滚动轴承故障诊断研究[D]. 武汉: 华中科技大学, 2019.

    LU Ziming. Research on fault diagnosis of rolling bearing based on NLM-VMD and metric learning[D]. Wuhan: Huazhong University of Science and Technology, 2019. (in Chinese)
    [22] MIRJALILI S,MIRJALILI S M,LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software,2014,69: 46-61. doi: 10.1016/j.advengsoft.2013.12.007
    [23] MIAO Yonghao,ZHAO Ming,LIN Jing,et al. Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings[J]. Mechanical Systems and Signal Processing,2017,92: 173-195. doi: 10.1016/j.ymssp.2017.01.033
    [24] 万书亭, 张雄, 豆龙江. 强噪源干扰下的滚动轴承复合故障分离方法研究[J]. 中南大学学报(自然科学版), 2018, 49(8): 1950-1959.

    WAN Shuting, ZHANG Xiong, DOU Longjiang. Separation of composite rolling bearings fault features with strong noise interference[J]. Journal of Central South University (Science and Technology), 2018, 49(8): 1950-1959. (in Chinese)
    [25] 田晶,王英杰,王志,等. 基于EEMD与空域相关降噪的滚动轴承故障诊断方法[J]. 仪器仪表学报,2018,39(7): 144-151.

    TIAN Jing,WANG Yingjie,WANG Zhi,et al. Fault diagnosis for rolling bearing based on EEMD and spatial correlation denoising[J]. Chinese Journal of Scientific Instrument,2018,39(7): 144-151. (in Chinese)
  • 加载中
图(21) / 表(7)
计量
  • 文章访问数:  92
  • HTML浏览量:  33
  • PDF量:  44
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-09-26
  • 网络出版日期:  2023-01-18

目录

    /

    返回文章
    返回