全矢改进连续谐波小波包滚动轴承故障特征提取

秦琴; 朱伏平; 杨方燕; 尹璐

doi:10.13433/j.cnki.1003-8728.20220167

全矢改进连续谐波小波包滚动轴承故障特征提取

doi: 10.13433/j.cnki.1003-8728.20220167

西南科技大学制造科学与工程学院四川绵阳　621010

详细信息

作者简介:
秦琴（1997−），硕士研究生，研究方向为设备故障预测，2606816707@qq.com

通讯作者:
朱伏平，副教授，硕士生导师，muff2001@163.com

中图分类号: TG156
计量
- 文章访问数: 637
- HTML全文浏览量: 27
- PDF下载量: 14
- 被引次数: 0
出版历程
- 收稿日期: 2021-10-07
- 刊出日期: 2023-12-25

Fault Feature Extraction of Rolling Bearings Based on Full Vector Improved Continuous Harmonic Wavelet Packet

School of Manufacturing Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, China

摘要

摘要: 滚动轴承在旋转类机械设备中运行时，会产生成分复杂的振动信号。现有滚动轴承信号处理方法多使用单通道信息，无法反映整个截面故障状态。本文提出了一种基于全矢改进连续谐波小波包变换的故障特征提取方法。首先使用相互正交的两个传感器，实现滚动轴承某一截面上双通道振动信号采集；其次利用全矢谱技术将所采集的同源双通道信号进行融合；然后使用改进连续谐波小波包变换分解融合后的信号；再从各子带中提取能反映各类故障特征的能量值组成特征向量；最后利用美国凯斯西储大学滚动轴承实验台的一组实测故障数据验证该方法的正确性。
- 滚动轴承 /
- 全矢谱 /
- 改进连续谐波小波包 /
- 特征提取
Abstract: When rolling bearings operate in rotating mechanical equipment, complex vibration signals will be measured, which can reflect equipment′s operation condition. The existing signal processing methods of rolling bearings mostly use single channel information, which can not reflect the fault state of the whole section. In this paper, a fault feature extraction method based on full vector improved continuous harmonic wavelet packet transform is proposed. Firstly, two orthogonal sensors are used to realize the dual channel vibration signal acquisition on a certain section of rolling bearing. Secondly, the collected homologous dual channel signals are fused with full vector spectrum technology. Then, the fused signal is decomposed by improved continuous harmonic wavelet packet transform. Next, the energy values that can reflect the characteristics of various faults are extracted from each subband to form a feature vector. Finally, a group of measured fault data from the rolling bearing test-bed of Case Western Reserve University in the United States are used to verify the correctness of the method.
- rolling bearing /
- full vector spectrum /
- improved continuous harmonic wavelet packet /
- feature extraction

HTML全文

图 1 谐波小波包变换分解的频域分布图

Figure 1. Frequency domain distribution of harmonic wavelet packet transform decomposition

下载: 全尺寸图片幻灯片

图 2 改进连续谐波小波包分解的频域分布图

Figure 2. Frequency domain distribution of improved continuous harmonic wavelet packet decomposition

下载: 全尺寸图片幻灯片

图 3 美国凯斯西储大学滚动轴承实验台

Figure 3. Rolling bearing test bench of Case Western Reserve University

下载: 全尺寸图片幻灯片

图 4 滚动轴承同源双通道振动信号时域波形图

Figure 4. Time domain waveform of homologous double-channel vibration signal of a rolling bearing

下载: 全尺寸图片幻灯片

图 5 滚动轴承同源双通道振动信号频谱图

Figure 5. Spectrum of identical double-channel vibration signal of a rolling bearing

下载: 全尺寸图片幻灯片

图 6 滚动轴承振动信号第八层分解得到的第4个频带及其频谱

Figure 6. The fourth frequency band and its spectrum obtained from the eighth layer decomposition of the vibration signal of a rolling bearing

下载: 全尺寸图片幻灯片

图 7 滚动轴承振动信号第八层分解得到的前4个频带频谱

Figure 7. Spectrum of the first 4 frequency bands obtained from the eighth layer decomposition of vibration signal of a rolling bearing

下载: 全尺寸图片幻灯片

图 8 轴承正常无故障状态能量直方图

Figure 8. Energy histogram of a bearing in normal and trouble-free state

下载: 全尺寸图片幻灯片

图 9 轴承外圈故障状态能量直方图

Figure 9. Histogram of energy of outer ring fault state of a bearing

下载: 全尺寸图片幻灯片

表 1 滚动轴承参数

Table 1. Rolling bearing parameters

内圈直径
D₀/mm 外圈直径
D₁/mm 节圆直径
D₂/mm 滚动体
直径d/mm 滚动
体数Z 接触角
α/（°）

25 32 39.04 7.94 9 0

下载: 导出CSV

参考文献(24)

[1]	郭俊超, 甄冬, 孟召宗, 等. 基于WAEEMD和MSB的滚动轴承故障特征提取[J]. 中国机械工程, 2021, 32(15): 1793-1800. doi: 10.3969/j.issn.1004-132X.2021.15.004 GUO J C, ZHEN D, MENG Z Z, et al. Feature extraction of rolling bearings based on WAEEMD and MSB[J]. China Mechanical Engineering, 2021, 32(15): 1793-1800. (in Chinese) doi: 10.3969/j.issn.1004-132X.2021.15.004
[2]	王涛, 张兵. 改进经验小波变换在滚动轴承故障特征提取中的应用[J]. 铁道机车车辆, 2019, 39(5): 53-58. doi: 10.3969/j.issn.1008-7842.2019.05.10 WANG T, ZHANG B. Application of improved empirical wavelet transform in fault feature extraction of rolling bearings[J]. Railway Locomotive & Car, 2019, 39(5): 53-58. (in Chinese) doi: 10.3969/j.issn.1008-7842.2019.05.10
[3]	刘鲲鹏, 苏涛, 赵磊, 等. 滚动轴承故障特征提取方法研究现状分析[J]. 内燃机与配件, 2018(24): 52-53. doi: 10.3969/j.issn.1674-957X.2018.24.022 LIU K P, SU T, ZHAO L, et al. Research status analysis of rolling bearing fault feature extraction methods[J]. Internal Combustion Engine & Parts, 2018(24): 52-53. (in Chinese) doi: 10.3969/j.issn.1674-957X.2018.24.022
[4]	张安, 马增强, 陈明义, 等. 基于奇异值分解和共振解调的滚动轴承故障特征提取[J]. 济南大学学报(自然科学版), 2019, 33(4): 289-294. doi: 10.13349/j.cnki.jdxbn.2019.04.002 ZHANG A, MA Z Q, CHEN M Y, et al. Bearing fault feature extraction method based on singular value decomposition and resonance demodulation[J]. Journal of University of Jinan (Science and Technology), 2019, 33(4): 289-294. (in Chinese) doi: 10.13349/j.cnki.jdxbn.2019.04.002
[5]	张焱, 何姝钡, 王平, 等. 无转速计下变工况滚动轴承故障特征量化表征提取[J]. 仪器仪表学报, 2021, 41(8): 104-114. doi: 10.19650/j.cnki.cjsi.J2107704 ZHANG Y, HE S B, WANG P, et al. Tacholess quantitative characterization of rolling bearing fault feature under varying conditions[J]. Chinese Journal of Scientific Instrument, 2021, 41(8): 104-114. (in Chinese) doi: 10.19650/j.cnki.cjsi.J2107704
[6]	李书乐, 马洁. 变工况下滚动轴承的故障特征提取[J]. 机械科学与技术, 2022, 41(1): 1-8. doi: 10.13433/j.cnki.1003-8728.20200307 LI S L, MA J. Fault feature extraction of rolling bearings under variable operating conditions[J]. Mechanical Science and Technology for Aerospace Engineering, 2022, 41(1): 1-8. (in Chinese) doi: 10.13433/j.cnki.1003-8728.20200307
[7]	WANG T Y, HAN Q K, CHU F L, et al. Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review[J]. Mechanical Systems and Signal Processing, 2019, 126: 662-685. doi: 10.1016/j.ymssp.2019.02.051
[8]	SMITH W A, BORGHESANI P, NI Q, et al. Optimal demodulation-band selection for envelope-based diagnostics: A comparative study of traditional and novel tools[J]. Mechanical Systems and Signal Processing, 2019, 134: 106303. doi: 10.1016/j.ymssp.2019.106303
[9]	LU S L, YAN R Q, LIU Y B, et al. Tacholess speed estimation in order tracking: A review with application to rotating machine fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 68(7): 2315-2332. doi: 10.1109/TIM.2019.2902806
[10]	宿磊, 黄海润, 李可, 等. 基于LCD-MCKD的滚动轴承故障特征提取方法[J]. 华中科技大学学报(自然科学版), 2019, 47(9): 19-24. doi: 10.13245/j.hust.190904 SU L, HUANG H R, LI K, et al. Feature extraction of fault rolling bearings based on LCD-MCKD[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2019, 47(9): 19-24. (in Chinese) doi: 10.13245/j.hust.190904
[11]	何俊, 杨世锡, 甘春标. 一类滚动轴承振动信号特征提取与模式识别[J]. 振动、测试与诊断, 2017, 37(6): 1181-1186. doi: 10.16450/j.cnki.issn.1004-6801.2017.06.017 HE J, YANG S X, GAN C B. Feature extraction and pattern recognition of vibration signals in a rolling bearing[J]. Journal of Vibration, Measurement & Diagnosis, 2017, 37(6): 1181-1186. (in Chinese) doi: 10.16450/j.cnki.issn.1004-6801.2017.06.017
[12]	李舜酩, 李香莲. 振动信号的现代分析技术与应用[M]. 北京: 国防工业出版社, 2008. LI S M, LI X L. Modern analysis techniques and application of vibration signals[M]. Beijing: National Defense Industry Press, 2008. (in Chinese)
[13]	HAN X, XIONG J Q, SUN R, et al. Notice of retraction: research on the roller bearing fault diagnosis based on Morphological Wavelet and LSSVM algorithm[C]//2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering. Chengdu: IEEE, 2013: 1888-1892.
[14]	唐贵基, 邓飞跃. 基于改进谐波小波包分解的滚动轴承复合故障特征分离方法[J]. 仪器仪表学报, 2015, 36(1): 143-151. doi: 10.19650/j.cnki.cjsi.2015.01.020 TANG G J, DENG Y F. Compound fault features separation method of rolling element bearing based on improved harmonic wavelet packet decomposition[J]. Chinese Journal of Scientific Instrument, 2015, 36(1): 143-151. (in Chinese) doi: 10.19650/j.cnki.cjsi.2015.01.020
[15]	田福庆, 罗荣. 改进的谐波小波包变换及其在弱故障特征提取中的应用[J]. 振动与冲击, 2013, 32(17): 29-34. doi: 10.3969/j.issn.1000-3835.2013.17.006 TIAN F Q, LUO R. Improved harmonic wavelet packet transformation and its application in weak fault feature extraction[J]. Journal of Vibration and Shock, 2013, 32(17): 29-34. (in Chinese) doi: 10.3969/j.issn.1000-3835.2013.17.006
[16]	王玉田, 严冰, 张淑清, 等. 一种改进的广义谐波小波包分解算法及在信号特征提取中的应用[J]. 燕山大学学报, 2013, 37(4): 358-365. doi: 10.3969/j.issn.1007-791X.2013.04.011 WANG Y T, YANG B, ZHANG S Q, et al. An improved generalized harmonic wavelet packet algorithm and its application in signal feature extraction[J]. Journal of Yanshan University, 2013, 37(4): 358-365. (in Chinese) doi: 10.3969/j.issn.1007-791X.2013.04.011
[17]	XU T, LIU Y, PEI A L, et al. The roller bearing fault diagnosis methods with harmonic wavelet packet and multi-classification relevance vector machine[J]. Journal of Vibroengineering, 2015, 17(6): 2962-2976.
[18]	张淑清, 马跃, 李盼, 等. 基于改进的广义谐波小波包分解和混沌振子的小电流接地系统故障选线[J]. 电工技术学报, 2015, 30(3): 13-20. doi: 10.3969/j.issn.1000-6753.2015.03.002 ZHANG S Q, MA Y, LI P, et al. Application of improved generalized harmonic wavelet packet decomposition and chaos oscillator to fault line detection in small current grounding system[J]. Transactions of China Electrotechnical Society, 2015, 30(3): 13-20. (in Chinese) doi: 10.3969/j.issn.1000-6753.2015.03.002
[19]	韩捷, 石来德. 全矢谱技术及工程应用[M]. 北京: 机械工业出版社, 2008. HAN J, SHI L D. Full vector spectrum technology and engineering application[M]. Beijing: China Machine Press, 2008. (in Chinese)
[20]	谢远东, 雷文平, 韩捷, 等. 全矢RNN的轴承故障诊断研究[J]. 机械设计与制造, 2021(9): 27-31. doi: 10.3969/j.issn.1001-3997.2021.09.007 XIE Y D, LEI W P, HAN J, et al. Research on bearing fault diagnosis using full vector RNN[J]. Machinery Design & Manufacture, 2021(9): 27-31. (in Chinese) doi: 10.3969/j.issn.1001-3997.2021.09.007
[21]	GONG X Y, DING L L, DU W L, et al. Gear fault diagnosis using dual channel data fusion and EEMD method[J]. Procedia Engineering, 2017, 174: 918-926. doi: 10.1016/j.proeng.2017.01.242
[22]	YU H, LI H R, LI Y L, et al. A novel improved full vector spectrum algorithm and its application in multi-sensor data fusion for hydraulic pumps[J]. Measurement, 2019, 133: 145-161. doi: 10.1016/j.measurement.2018.10.011
[23]	高山, 周玉平, 陈宏, 等. 全矢HMM在轴承剩余寿命预测中的应用[J]. 机械设计与制造, 2020(12): 64-67. doi: 10.3969/j.issn.1001-3997.2020.12.014 GAO S, ZHOU Y P, CHEN H, et al. The prediction of residual life of full vector HMM bearing based on KPCA[J]. Machinery Design & Manufacture, 2020(12): 64-67. (in Chinese) doi: 10.3969/j.issn.1001-3997.2020.12.014
[24]	Case Western Reserve University Bearing Data Center. Fault bearing test data[EB/OL]. (2021-05-25). http//csegroups. case. edu/bearindatacenter/hom