Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDA

Fuwang Liang; Huer Sun; Kexin Liu

doi:10.16578/j.issn.1004.2539.2021.02.024

您当前的位置：

首页 >

文章列表页 >

Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDA

Development·Application | 更新时间：2022-11-10

- Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDA
- Journal of Mechanical Transmission Vol. 45, Issue 2, Pages: 157-162(2021)
- 作者机构：
  
  1.中北大学机械工程学院，山西太原 030051
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2021.02.024
  CLC：
扫描看全文
梁富旺,孙虎儿,刘柯欣.基于SK-MOMEDA的滚动轴承微弱故障特征提取[J].机械传动,2021,45(02):157-162.

Liang Fuwang,Sun Huer,Liu Kexin.Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDA[J].Journal of Mechanical Transmission,2021,45(02):157-162.
梁富旺,孙虎儿,刘柯欣.基于SK-MOMEDA的滚动轴承微弱故障特征提取[J].机械传动,2021,45(02):157-162. DOI： 10.16578/j.issn.1004.2539.2021.02.024.

Liang Fuwang,Sun Huer,Liu Kexin.Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDA[J].Journal of Mechanical Transmission,2021,45(02):157-162. DOI： 10.16578/j.issn.1004.2539.2021.02.024.

摘要

针对滚动轴承早期周期性瞬态冲击不明显及谱峭度在低信噪比情况下分析效果差的问题，提出多点优化最小熵解卷积（Multipoint optimal minimum entropy deconvolution adjusted，MOMEDA）和谱峭度相结合的轴承微弱故障特征提取方法。首先，采用MOMEDA作为前置滤波器对含有强噪声的微弱故障冲击信号进行降噪，突显信号中的周期性冲击性成分；然后，通过谱峭度分析，以最佳中心频率和带宽对降噪的信号进行带通滤波；最后，对滤波后的信号进行Hilbert包络谱分析，便可以准确地获得轴承信号的故障特征频率。仿真信号和实验分析结果表明，该方法可有效增强振动信号的周期性瞬态冲击特征，提取出滚动轴承早期微弱故障特征。

Abstract

Aiming at the problem that the early periodic transient impulse of rolling bearings is not obvious and the spectral kurtosis is poorly analyzed under low signal-to-noise ratio， a method of extracting the weak fault features of rolling bearing based on the combination of multipoint optimal minimum entropy deconvolution adjusted （MOMEDA） and spectral kurtosis is proposed. Firstly， MOMEDA is used as the prefilter to reduce the noise of weak fault impulse signal with strong noise and highlight the periodic impulse component in the signal. Then， through spectral kurtosis analysis， the denoised signal is filtered under the optimal center frequency and bandwidth. Finally， the fault characteristic frequency of bearing signal can be accurately obtained by Hilbert envelope spectrum analysis of filtered signal. The simulation and experimental results show that the method can effectively enhance the periodic transient impulse characteristics of vibration signals and extract the early weak fault characteristics of rolling bearing.

关键词

滚动轴承多点优化最小熵解卷积谱峭度微弱故障特征提取

Keywords

Rolling bearingMultipoint optimal minimum entropy deconvolution adjustedSpectral KurtosisWeak faultFeature extraction

references

陈强强，戴邵武，戴洪德，等.滚动轴承故障诊断方法综述［J］.仪表技术，2019（9）：1-4.

CHEN Qiangqiang，DAI Shaowu，DAI Hongde，et al.Review on fault diagnosis on the rolling bearing［J］.Instrument Technology，2019（9）：1-4.

刘尚坤，唐贵基，何玉灵.Teager能量算子结合MCKD的滚动轴承早期故障识别［J］.振动与冲击，2016，35（15）：98-102.

LIU Shangkun，TANG Guiji，HE Yuling.Incipient fault diagnosis of rolling bearings based on Teager energy operator and MCKD［J］.Vibration and Shock，2016，35（15）：98-102.

WIGGINS R A.Minimum entropy deconvolution［J］.Geoexploration，1978，16（1/2）：21-35.

DWYER R.Detection of non-Gaussian signals by frequency domain Kurtosis estimation［C］//Proceedings of the IEEE International Conference on Acoustics，Speech，and Signal Processing（ICASSP），April 14-16，1983，Boston，Massachusetts.New York：IEEE，1983：607-610.

ANTONI J.The spectral kurtosis：a useful tool for characterising non-stationary signals［J］.Mechanical Systems & Signal Processing，2004，20（2）：282-307.

赵见龙，张永超，王立夫，等.基于共振稀疏分解与谱峭度的滚动轴承故障诊断［J］.组合机床与自动化加工技术，2019（4）：111-115.

ZHAO Jianlong，ZHANG Yongchao，WANG Lifu，et al. Rolling bearing fault diagnosis based on resonance sparse decomposition and spectral kurtosis［J］.Combined Machine Tool and Automatic Processing Technology，2019（4）：111-115.

肖凌俊，吕勇，袁锐.基于SAS降噪和谱峭度的特征频率提取方法［J］.机械传动，2019，43（12）：109-115.

XIAO Lingjun，LÜ Yong，YUAN Rui.Feature frequency extraction method based on SAS noise reduction and spectral kurtosis［J］.Journal of Mechanical Transmission，2019，43（12）：109-115.

MCDONALD G L，ZHAO Q.Multipoint optimal minimum entropy deconvolution and convolution fix：application to vibration fault detection［J］.Mechanical Systems and Signal Processing，2017，82（1）：461-477．

武超，孙虎儿，梁晓华.基于MOMEDA和包络谱的齿轮微弱故障特征提取［J］.机械传动，2018，42（3）：164-168.

WU Chao，SUN huer，LIANG Xiaohua.Feature extraction of weak fault for gear based on MOMEDA and envelope spectrum［J］.Journal of Mechanical Transmission，2018，42（3）：164-168.

JENA D P，PANIGRAHI S N，KUMAR R. Gear fault identification and localization using analytic wavelet transform of vibration signal［J］.Measurement Journal of the International Measurement Confederation，2013，46（3）：1115-1124.

ANTONI J，RANDALL R B.The spectral kurtosis：application to the vibratory surveillance and diagnostics of rotating machines［J］.Mechanical Systems & Signal Processing，2004，20（2）：308-331.

刘海洋.基于谱峭度的信号特征提取及其传动系统关键部件故障诊断应用［D］.苏州：苏州大学，2013：46-47.

LIU Haiyang.The extraction of signal feature based on spectral kurtosis and its application in the fault diagnosis of crucial components in transmission system［D］.Suzhou：Suzhou University，2013：46-47.

Case Western Reserve University Bearing Data Center.Seeded fault test date［DB/OL］.［2020-04-18］.http：//csegroups.case.edu/bearingdatacenter/homehttp://csegroups.case.edu/bearingdatacenter/home．

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Feature Extraction of Weak Fault for Rolling Bearing based on Improved SSD Denoising

Feature Extraction of Weak Fault for Rolling Bearing based on CYCBD and Envelope Spectrum

Bearing Feature Extraction Method Based on the Time Subsequence

Feature Extraction of Weak Fault of Rolling Bearing based on TVF-EMD and TEO

Fault Feature Extraction for Rolling Bearing based on LMD Energy Entropy

Related Author

No data

Related Institution

School of Information Management, Wuxi Institute of Communications Technology

IOT Engineering College, Wuxi City College of Vocational Technology

Jiangsu Normal University

School of Mechanical Engineering, North University of China

School of Mechanical Engineering, Zhengzhou University

⁰