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复杂传递路径下轴承滚动体故障特征提取

邓武 李凌锋 李伟含 赵慧敏

邓武, 李凌锋, 李伟含, 赵慧敏. 复杂传递路径下轴承滚动体故障特征提取[J]. 交通运输工程学报, 2023, 23(1): 184-194. doi: 10.19818/j.cnki.1671-1637.2023.01.014
引用本文: 邓武, 李凌锋, 李伟含, 赵慧敏. 复杂传递路径下轴承滚动体故障特征提取[J]. 交通运输工程学报, 2023, 23(1): 184-194. doi: 10.19818/j.cnki.1671-1637.2023.01.014
DENG Wu, LI Ling-feng, LI Wei-han, ZHAO Hui-min. Fault feature extraction of bearing rolling elements under complex transmission path[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 184-194. doi: 10.19818/j.cnki.1671-1637.2023.01.014
Citation: DENG Wu, LI Ling-feng, LI Wei-han, ZHAO Hui-min. Fault feature extraction of bearing rolling elements under complex transmission path[J]. Journal of Traffic and Transportation Engineering, 2023, 23(1): 184-194. doi: 10.19818/j.cnki.1671-1637.2023.01.014

复杂传递路径下轴承滚动体故障特征提取

doi: 10.19818/j.cnki.1671-1637.2023.01.014
基金项目: 

国家自然科学基金项目 51605068

国家自然科学基金项目 51475065

国家自然科学基金项目 61771087

详细信息
    作者简介:

    邓武(1976-),男,四川安岳人,中国民航大学教授,工学博士,从事状态检测和故障诊断、智能优化与信息处理研究

    通讯作者:

    赵慧敏(1977-), 女,黑龙江富锦人, 中国民航大学教授,工学博士

  • 中图分类号: U269.5

Fault feature extraction of bearing rolling elements under complex transmission path

Funds: 

National Natural Science Foundation of China 51605068

National Natural Science Foundation of China 51475065

National Natural Science Foundation of China 61771087

More Information
  • 摘要: 为消除复杂传递路径对轴承滚动体振动信号的影响并提高故障特征提取的能力,研究了基于变分模态分解(VMD)、优化最大相关峭度解卷积(MCKD)和1.5维谱的轴承滚动体故障特征提取问题;分析了轴承滚动体原始振动信号特点、早期故障信号的特性以及复杂传递路径对振动信号的影响,运用VMD将原始振动信号分解为一系列本征模态函数(IMFs),提出了转频分量剔除方法,通过峭度准则优选2个峭度较大的IMFs分量进行重构;基于网格搜索法研究了MCKD算法参数优化方法,用以增强重构信号的周期性故障特征,消除复杂传递路径对轴承滚动体故障信号的影响;利用1.5维谱分析重构信号,建立了复杂传递路径下轴承滚动体故障特征提取新方法,实现了轴承滚动体故障的准确诊断;为了证明方法的有效性,选取美国凯斯西储大学轴承SKF6205基座滚动体数据进行试验验证与分析。试验结果表明:网格搜索法获得了MCKD算法的最优滤波长度与冲击周期参数(365、85),优化MCKD算法增强了重构信号的故障特征,减少了无关频率分量,明显降低了其他成分的干扰;提出的故障特征提取方法在0、735和1 470 W负载条件下均提取到了轴承滚动体的故障特征频率(140.6 Hz)以及二倍频(281.3 Hz)和三倍频(421.9 Hz)等所有倍频分量,且不受负载条件的影响,消除了复杂传递路径对轴承滚动体故障特征提取的影响。可见,提取方法可以有效解决复杂传递路径下轴承滚动体故障特征提取与诊断问题。

     

  • 图  1  轴承滚动体故障特征提取流程

    Figure  1.  Fault feature extraction process of bearing rolling elements

    图  2  滚动轴承故障模拟试验台

    Figure  2.  Fault simulation experiment platform of rolling bearing

    图  3  滚动体信号时域和功率谱

    Figure  3.  Time-domain and power spectrum of rolling element signal

    图  4  各IMF的功率谱

    Figure  4.  Power spectra of each IMF

    图  5  重构信号的时域和功率谱

    Figure  5.  Time-domain and power spectra of reconstructed signal

    图  6  解卷积信号的1.5维谱

    Figure  6.  1.5-dimensional spectra of deconvolution signal

    图  7  解卷积信号的功率谱

    Figure  7.  Power spectra of deconvolution signal

    图  8  未剔除转频分量的解卷积信号

    Figure  8.  Deconvolution signals with containing frequency conversion component

    图  9  负载为735 W条件下轴承滚动体故障信号

    Figure  9.  Fault signals of bearing rolling elements under 735 W

    图  10  负载为1 470 W条件下的轴承滚动体故障信号

    Figure  10.  Fault signals of bearing rolling elements under 1 470 W

    表  1  IMF 1~IMF 7的峭度值和频率分量

    Table  1.   Kurtosis values and frequency components of each IMF 1-IMF 7

    参数 IMF 1 IMF 2 IMF 3 IMF 4 IMF 5 IMF 6 IMF 7
    峭度值 2.539 1 3.297 1 2.134 2 3.039 3 2.766 7 1.711 2 1.991 1
    频率/Hz 199.20 29.30 70.31 58.59 181.60 58.59 58.59
    下载: 导出CSV

    表  2  功率谱和1.5维谱中的频率分量

    Table  2.   Frequency components of power spectra and 1.5-dimensional spectra Hz

    排序 1 2 3 4 5 6 7
    功率谱 5.9 146.5 281.3 421.9 568.4 709.0 855.5
    1.5维谱 5.9 140.6 281.3 421.9 562.5 703.1 843.8
    下载: 导出CSV
  • [1] ZHAO Hui-min, SUN Meng, DENG Wu, et al. A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing[J]. Entropy, 2016, 19(1): 14. doi: 10.3390/e19010014
    [2] 刘长良, 武英杰, 甄成刚. 基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J]. 中国电机工程学报, 2015, 35(13): 3358-3365. doi: 10.13334/j.0258-8013.pcsee.2015.13.020

    LIU Chang-liang, WU Ying-jie, ZHEN Cheng-gang. Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C-means clustering[J]. Proceedings of the CSEE, 2015, 35(13): 3358-3365. (in Chinese) doi: 10.13334/j.0258-8013.pcsee.2015.13.020
    [3] 唐贵基, 王晓龙. 参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J]. 西安交通大学学报, 2015, 49(5): 73-81. https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT201505012.htm

    TANG Gui-ji, WANG Xiao-long. Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing[J]. Journal of Xi'an Jiaotong University, 2015, 49(5): 73-81. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT201505012.htm
    [4] DENG Wu, LIU Hao-dong, ZHANG Sheng-jie, et al. Research on an adaptive variational mode decomposition with double thresholds for feature extraction[J]. Symmetry, 2018, 10(12): 684. doi: 10.3390/sym10120684
    [5] CHANDRA N H, SEKHAR A S. Fault detection in rotor bearing systems using time frequency techniques[J]. Mechanical Systems and Signal Processing, 2016, 72/73: 105-133. doi: 10.1016/j.ymssp.2015.11.013
    [6] 陈果, 贺志远, 尉询楷, 等. 基于整机的中介轴承外圈剥落故障振动分析[J]. 航空动力学报, 2020, 35(3): 658-672. doi: 10.13224/j.cnki.jasp.2020.03.022

    CHEN Guo, HE Zhi-yuan, WEI Xun-kai, et al. Vibration analysis of peeling fault of intermediate bearing out ring based on whole aero-engine[J]. Journal of Aerospace Power, 2020, 35(3): 658-672. (in Chinese) doi: 10.13224/j.cnki.jasp.2020.03.022
    [7] WANG W, LEE H W. An energy kurtosis demodulation technique for signal denoising and bearing fault detection[J]. Measurement Science and Technology, 2013, 24(2): 025601. doi: 10.1088/0957-0233/24/2/025601
    [8] 邢欣, 崔亚辉, 刘晓琳, 等. 一种自适应提取有效信号的滚动轴承故障诊断方法[J]. 噪声与振动控制, 2018, 38(2): 150-153, 161. doi: 10.3969/j.issn.1006-1355.2018.02.029

    XING Xin, CUI Ya-hui, LIU Xiao-lin, et al. A fault diagnosis method for rolling bearings based on adaptive extraction of effective signals[J]. Noise and Vibration Control, 2018, 38(2): 150-153, 161. (in Chinese) doi: 10.3969/j.issn.1006-1355.2018.02.029
    [9] LI Hua, LIU Tao, WU Xing, et al. Enhanced frequency band entropy method for fault feature extraction of rolling element bearings[J]. IEEE Transactions on Industrial Informatics, 2020, 16(9): 5780-5791. doi: 10.1109/TII.2019.2957936
    [10] ZHOU Hao-xuan, LI Hua, LIU Tao, et al. A weak fault feature extraction of rolling element bearing based on attenuated cosine dictionaries and sparse feature sign search[J]. ISA Transactions, 2020, 97: 143-154. doi: 10.1016/j.isatra.2019.08.013
    [11] 牛一捷, 李花, 邓武, 等. 基于TQWT稀疏表示的滚动轴承故障诊断方法[J]. 交通运输工程学报, 2021, 21(6): 237-246. doi: 10.19818/j.cnki.1671-1637.2021.06.018

    NIU Yi-jie, LI Hua, DENG Wu, et al. Rolling bearing fault diagnosis method based on TQWT and sparse representation[J]. Journal of Traffic and Transportation Engineering, 2021, 21(6): 237-246. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2021.06.018
    [12] 张龙, 甄灿壮, 熊国良, 等. 基于深度时频特征的机车轴承故障诊断[J]. 交通运输工程学报, 2021, 21(6): 247-258. doi: 10.19818/j.cnki.1671-1637.2021.06.019

    ZHANG Long, ZHEN Can-zhuang, XIONG Guo-liang, et al. Locomotive bearing fault diagnosis based on deep time-frequency features[J]. Journal of Traffic and Transportation Engineering, 2021, 21(6): 247-258. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2021.06.019
    [13] 袁旻忞, SHEN A, 鲁帆, 等. 高速列车运行工况下噪声传递路径及声源贡献量分析[J]. 振动与冲击, 2013, 32(21): 189-196. doi: 10.3969/j.issn.1000-3835.2013.21.033

    YUAN Min-min, SHEN A, LU Fan, et al. Operational transfer path analysis and noise sources contribution for China railway high-speed (CRH)[J]. Journal of Vibration and Shock, 2013, 32(21): 189-196. (in Chinese) doi: 10.3969/j.issn.1000-3835.2013.21.033
    [14] 张磊, 李彬, 杨自春, 等. 融合盲源分离的船舶耦合振源传递路径分析技术研究[J]. 振动与冲击, 2020, 39(17): 150-156. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202017021.htm

    ZHANG Lei, LI Bin, YANG Zi-chun, et al. TPA technique for ship coupled vibration sources based on BSS[J]. Journal of Vibration and Shock, 2020, 39(17): 150-156. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202017021.htm
    [15] WIGGINS R A. Minimum entropy deconvolution[J]. Geoexploration, 1978, 16(1/2): 21-35.
    [16] MCDONALD G L, ZHAO Q, ZUO M J. Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection[J]. Mechanical Systems and Signal Processing, 2012, 33: 237-255. doi: 10.1016/j.ymssp.2012.06.010
    [17] 夏均忠, 赵磊, 白云川, 等. 基于MCKD和VMD的滚动轴承微弱故障特征提取[J]. 振动与冲击, 2017, 36(20): 78-83. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201720013.htm

    XIA Jun-zhong, ZHAO Lei, BAI Yun-chuan, et al. Feature extraction for rolling element bearing weak fault based on MCKD and VMD[J]. Journal of Vibration and Shock, 2017, 36(20): 78-83. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201720013.htm
    [18] XU Z, QIN C, TANG G. A novel deconvolution cascaded variational mode decomposition for weak bearing fault detection with unknown signal transmission path[J]. IEEE Sensors Journal, 2021, 21(2): 1746-1755.
    [19] 向玲, 张力佳. 基于VMD和1. 5维Teager能量谱的滚动轴承故障特征提取[J]. 振动与冲击, 2017, 36(18): 98-104, 124. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201718015.htm

    XIANG Ling, ZHANG Li-jia. Rolling bearing fault feature extraction based on the VMD and 1. 5-dimensional Teager energy spectrum[J]. Journal of Vibration and Shock, 2017, 36(18): 98-104, 124. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201718015.htm
    [20] 王望望, 邓林峰, 赵荣珍, 等. 基于二次聚类分割与Teager能量谱的滚动轴承微弱故障特征提取[J]. 振动与冲击, 2020, 39(13): 246-253. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202013036.htm

    WANG Wang-wang, DENG Lin-feng, ZHAO Rong-zhen, et al. Weak fault feature extraction of rolling bearing based on secondary clustering segmentation and Teager energy spectrum[J]. Journal of Vibration and Shock, 2020, 39(13): 246-253. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202013036.htm
    [21] 唐贵基, 王晓龙. 自适应最大相关峭度解卷积方法及其在轴承早期故障诊断中的应用[J]. 中国电机工程学报, 2015, 35(6): 1436-1444. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201506019.htm

    TANG Gui-ji, WANG Xiao-long. Adaptive maximum correlated kurtosis deconvolution method and its application on incipient fault diagnosis of bearing[J]. Proceedings of the CSEE, 2015, 35(6): 1436-1444. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201506019.htm
    [22] 杨斌, 张家玮, 樊改荣, 等. 最优参数MCKD与ELMD在轴承复合故障诊断中的应用研究[J]. 振动与冲击, 2019, 38(11): 59-67. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201911011.htm

    YANG Bin, ZHANG Jia-wei, FAN Gai-rong, et al. Application of MCKD and ELMD in bearing compound fault diagnosis[J]. Journal of Vibration and Shock, 2019, 38(11): 59-67. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201911011.htm
    [23] 沈长青, 王旭, 王冬, 等. 基于多尺度卷积类内迁移学习的列车轴承故障诊断[J]. 交通运输工程学报, 2020, 50(5): 151-164. doi: 10.19818/j.cnki.1671-1637.2020.05.012

    SHEN Chang-qing, WANG Xu, WANG Dong, et al. Multi-scale convolution intra-class transfer learning for train bearing fault diagnosis[J]. Journal of Traffic and Transportation Engineering, 2020, 50(5): 151-164. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.05.012
    [24] 江星星, 宋秋昱, 朱忠奎, 等. 基于收敛趋势变分模式分解的齿轮箱故障诊断方法[J]. 交通运输工程学报, 2022, 22(1): 177-189. doi: 10.19818/j.cnki.1671-1637.2022.01.015

    JIANG Xing-xing, SONG Qiu-yu, ZHU Zhong-kui, et al. Gearbox fault diagnosis method based on convergent trend-guided variational mode decomposition[J]. Journal of Traffic and Transportation Engineering, 2022, 22(1): 177-189. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2022.01.015
    [25] YAN Xiao-an, ZHANG Wan, JIA Min-ping. A bearing fault feature extraction method based on optimized singular spectrum decomposition and linear predictor[J]. Measurement Science and Technology, 2021, 32(11): 115023.
    [26] WANG Cong, GAN Meng, ZHU Chang-an. Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory[J]. Journal of Intelligent Manufacturing, 2018, 29(4): 937-951.
    [27] WANG Ran, FANG Hai-tao, ZHANG Yong-li, et al. Low-rank enforced fault feature extraction of rolling bearings in a complex noisy environment: a perspective of statistical modeling of noises[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 3510414.
    [28] ZHANG Wan, YAN Xiao-an, JIA Min-ping. Sparse enhancement based on the total variational denoising for fault feature extraction of rolling element bearings[J]. Measurement, 2022, 195: 111163.
    [29] ZHU Dan-chen, CHEN Ji-heng, YIN Bo-long. Fault feature extraction of rolling element bearing based on TPE-EVMD[J]. Measurement, 2021, 183: 109880.
    [30] KE Yun, SONG En-zhe, CHEN Yan-zhen, et al. Multiscale bidirectional diversity entropy for diesel injector fault-type diagnosis and fault degree diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 6503410.
    [31] WANG Cun-jun, XU Zi-li. An intelligent fault diagnosis model based on deep neural network for few-shot fault diagnosis[J]. Neurocomputing, 2021, 456: 550-562.
    [32] HUANG Ting, ZHANG Qiang, TANG Xiao-an, et al. A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems[J]. Artificial Intelligence Review, 2022, 55(2): 1289-1315.
    [33] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
    [34] 吴小涛, 杨锰, 袁晓辉, 等. 基于峭度准则EEMD及改进形态滤波方法的轴承故障诊断[J]. 振动与冲击, 2015, 34(2): 38-44. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201502007.htm

    WU Xiao-tao, YANG Meng, YUAN Xiao-hui, et al. Bearing fault diagnosis using EEMD and improved morphological filtering method based on kurtosis criterion[J]. Journal of Vibration and Shock, 2015, 34(2): 38-44. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201502007.htm
    [35] DENG Wu, LIU Hai-long, XU Jun-jie, et al. An improved quantum-inspired differential evolution algorithm for deep belief network[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(10): 7319-7327.
    [36] ZHAO Hui-min, LIU Hao-dong, XU Jun-jie, et al. Performance prediction using high-order differential mathematical morphology gradient spectrum entropy and extreme learning machine[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(7): 4165-4172.
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  • 收稿日期:  2022-09-21
  • 网络出版日期:  2023-03-08
  • 刊出日期:  2023-02-25

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