利用FDM和随机共振检测风机主轴承微弱旋转信号

段皓然; 张超; 张彪

doi:10.13433/j.cnki.1003-8728.20200184

利用FDM和随机共振检测风机主轴承微弱旋转信号

doi: 10.13433/j.cnki.1003-8728.20200184

内蒙古科技大学机械工程学院, 内蒙古包头 014010

基金项目:

国家自然科学基金项目 51565046

国家自然科学基金项目 51965052

详细信息

作者简介:
段皓然(1992-), 硕士研究生, 研究方向为振动信号处理及旋转机械故障诊断, Danielhaha@foxmail.com

通讯作者:
张超, 教授, 博士生导师, Zhanghero123@163.com

中图分类号: TG156
计量
- 文章访问数: 129
- HTML全文浏览量: 39
- PDF下载量: 14
- 被引次数: 0
出版历程
- 收稿日期: 2019-02-28
- 刊出日期: 2021-07-01

Weak Rotating Signal Detection of Wind Turbine Main Bearing Using FDM and Stochastic Resonance

School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Bao'tou 014010, Inner Mongolia, China

摘要

摘要: 轴承作为风力发电机设备中重要部件, 其健康状态直接影响风力发电机运行的稳定性和现场的安全可靠性。由于风力发电机特殊的工作环境, 导致采集到的振动信号中包含大量的噪声干扰, 难以准确提取轴承振动信号包含的信息成分, 给评估主轴承健康状态带来困难。因此本文采用将傅立叶分解(Fourier decomposition method, FDM)和随机共振(Stochastic resonance, SR)相结合的方式提取信号中微弱的轴承振动信息。首先用FDM将原始信号自适应地分解为一系列包含轴承振动特征的傅立叶频带函数, 然后找出相关性大的频带函数进行重构, 最后采用SR对重构信号进行分析获得特征频率, 判断轴承的健康状态。结果显示, 将两种方法相结合能有效提高输出信噪比, 提升特征频率检测的精度, 为实现风机轴承早期微弱故障诊断提供帮助。
- 风力发电机 /
- 轴承 /
- 振动信号 /
- 傅里叶分解 /
- 随机共振 /
- 信噪比
Abstract: Bearings are an important component of wind turbines, and their health directly affects the stability of wind turbine operation and the reliability of the work site. Due to the special working environment of wind turbines, the collected vibration signals contain a large amount of noise interference, it is difficult to accurately extract the information contained in the bearing vibration signals, and also hard to assess the health status of the main bearing. Therefore, in this paper, the Fourier decomposition method (FDM) and Stochastic resonance (SR) are combined to extract the weak bearing vibration information in the signals. Firstly, FDM is used to adaptively decompose the original signals into a series of Fourier intrinsic band functions (FIBFS), which containing bearing vibration characteristics, then find the FIBFS with large correlation for reconstruction, and finally use SR to analyze the reconstructed signals to obtain the characteristic frequency. The results show that the combination of the two methods can effectively improve the output signal to noise ratio, improve the accuracy of the characteristic frequency detection, and provide help for the early diagnosis of weak faults in wind turbine bearings.
- wind turbines /
- bearing /
- vibration signals /
- FDM /
- stochastic resonance /
- signal to noise ratio

HTML全文

图 1 诊断流程图

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图 2 SR处理后的时域波形

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图 3 SR处理后的频域波形

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图 4 SR处理后局部放大的频域波形

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图 5 FDM处理后的频域波形

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图 6 FDM处理后局部放大的频域波形

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图 7 SR和FDM处理后的时域波形

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图 8 SR和FDM处理后的频域波形

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图 9 SR和FDM处理后局部放大的频域波形

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图 10 风机检测系统测点安装位置

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图 11 齿轮箱输入轴轴承信号时域波形

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图 12 齿轮箱输入轴轴承信号频域波形

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图 13 SR处理齿轮箱输入轴轴承信号时域波形

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图 14 SR处理齿轮箱输入轴轴承信号频域波形

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图 15 FDM处理得到的频域图

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图 16 重构信号时域波形

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图 17 SR和FDM处理后得到的时域波形

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图 18 SR和FDM处理后得到的频域局部放大波形

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参考文献(18)

[1]	YANG J H, HUANG D W, ZHOU D J, et al. Optimal IMF selection and unknown fault feature extraction for rolling bearings with different defect modes[J]. Measurement, 2020, 157: 107660 doi: 10.1016/j.measurement.2020.107660
[2]	SINGH H, PULIKOLLU R V, HAWKINS W, et al. Investigation of microstructural alterations in low-and high-speed intermediate-stage wind turbine gearbox bearings[J]. Tribology Letters, 2017, 65(3): 81 doi: 10.1007/s11249-017-0861-5
[3]	DENG F Y, QIANG Y W, LIU Y Q, et al. Adaptive parametric dictionary design of sparse representation based on fault impulse matching for rotating machinery weak fault detection[J]. Measurement Science and Technology, 2020, 31(6): 065101 doi: 10.1088/1361-6501/ab6f2f
[4]	CHEN F F, CHENG M T, TANG B P, et al. A novel optimized multi-kernel relevance vector machine with selected sensitive features and its application in early fault diagnosis for rolling bearings[J]. Measurement, 2020, 156: 107583 doi: 10.1016/j.measurement.2020.107583
[5]	LU S L, ZHENG P, LIU Y B, et al. Sound-aided vibration weak signal enhancement for bearing fault detection by using adaptive stochastic resonance[J]. Journal of Sound and Vibration, 2019, 449: 18-29 doi: 10.1016/j.jsv.2019.02.028
[6]	HUANG N E, SHEN Z, LONG S R, et al. 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
[7]	WU Z H, HUANG N E. Ensemble empirical mode decomposi-tion: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41 doi: 10.1142/S1793536909000047
[8]	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
[9]	BENZI R, PARISI G, SUTERA A, et al. A theory of stochastic resonance in climatic change[J]. SIAM Journal on Applied Mathematics, 1983, 43(3): 565-578 doi: 10.1137/0143037
[10]	KOJIMA N, LAMSAL B, MATSUMOTO N. Proposing autotuning image enhancement method using stochastic resonance[J]. Electronics and Communications in Japan, 2019, 102(4): 35-46 http://ci.nii.ac.jp/naid/40021726779
[11]	张晓飞, 胡茑庆, 胡雷, 等. 基于倒谱预白化和随机共振的轴承故障增强检测[J]. 机械工程学报, 2012, 48(23): 83-89 https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201223014.htm ZHANG X F, HU N Q, HU L, et al. Enhanced detection of bearing faults based on signal cepstrum pre-whitening and stochastic resonance[J]. Journal of Mechanical Engineering, 2012, 48(23): 83-89 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201223014.htm
[12]	李继猛, 陈雪峰, 何正嘉. 采用粒子群算法的冲击信号自适应单稳态随机共振检测方法[J]. 机械工程学报, 2011, 47(21): 58-63 https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201121010.htm LI J M, CHEN X F, HE Z J. Adaptive monostable stochastic resonance based on PSO with application in impact signal detection[J]. Journal of Mechanical Engineering, 2011, 47(21): 58-63 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201121010.htm
[13]	SINGH P, JOSHI S D, PATNEY R K, et al. The Fourier decomposition method for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2015, 473(2199): 20160871
[14]	付秀伟, 高兴泉. 基于傅里叶分解与奇异值差分谱的滚动轴承故障诊断方法[J]. 计量学报, 2018, 39(5): 688-692 doi: 10.3969/j.issn.1000-1158.2018.05.17 FU X W, GAO X Q. Rolling bearing fault diagnosis based on FDM and singular value difference spectrum[J]. Acta Metrologica Sinica, 2018, 39(5): 688-692 (in Chinese) doi: 10.3969/j.issn.1000-1158.2018.05.17
[15]	林近山, 窦春红, 赵光胜, 等. 基于傅里叶分解方法的风电齿轮箱故障诊断[J]. 机械传动, 2018, 42(11): 132-136 https://www.cnki.com.cn/Article/CJFDTOTAL-JXCD201811025.htm LIN J S, DOU C H, ZHAO S G, et al. Fault diagnosis of wind-turbine gearboxes based on fourier decomposition method[J]. Journal of Mechanical Transmission, 2018, 42(11): 132-136 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXCD201811025.htm
[16]	张国瑞, 王旭元, 郭文斌. 基于傅里叶分解与1.5维Teager能量谱的滚动轴承故障诊断方法[J]. 机械传动, 2017, 41(3): 191-196 https://www.cnki.com.cn/Article/CJFDTOTAL-JXCD201703038.htm ZHANG G R, WANG X Y, GUO W B. Fault diagnosis method of rolling bearing based on Fourier decomposition method and 1.5-dimensional Teager energy spectrum[J]. Journal of Mechanical Transmission, 2017, 41(3): 191-196 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXCD201703038.htm
[17]	JIANG X Y, LI S. BAS: beetle antennae search algorithm for optimization problems[J]. arXiv: 1710.10724, 2017 http://arxiv.org/abs/1710.10724
[18]	段佳雷. 基于分段线性非饱和随机共振的机械早期故障诊断方法研究[J]. 中国测试, 2017, 43(8): 106-112 https://www.cnki.com.cn/Article/CJFDTOTAL-SYCS201708022.htm DUAN J L. Study on incipient fault diagnosis of machinery based on piecewiselinearity and unsaturated stochastic resonance[J]. China Measurement & Testing Technology, 2017, 43(8): 106-112 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SYCS201708022.htm