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

Xupeng Wang; Huer Sun

doi:10.16578/j.issn.1004.2539.2022.03.025

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

Development·Application | 更新时间：2022-09-24

- Feature Extraction of Weak Fault for Rolling Bearing based on Improved SSD Denoising
- Journal of Mechanical Transmission Vol. 46, Issue 3, Pages: 163-169(2022)
- 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.1004.2539.2022.03.025
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王续鹏,孙虎儿.基于改进SSD降噪的滚动轴承故障特征提取[J].机械传动,2022,46(03):163-169.

Wang Xupeng,Sun Huer.Feature Extraction of Weak Fault for Rolling Bearing based on Improved SSD Denoising[J].Journal of Mechanical Transmission,2022,46(03):163-169.
王续鹏,孙虎儿.基于改进SSD降噪的滚动轴承故障特征提取[J].机械传动,2022,46(03):163-169. DOI： 10.16578/j.issn.1004.2539.2022.03.025.

Wang Xupeng,Sun Huer.Feature Extraction of Weak Fault for Rolling Bearing based on Improved SSD Denoising[J].Journal of Mechanical Transmission,2022,46(03):163-169. DOI： 10.16578/j.issn.1004.2539.2022.03.025.

摘要

针对强背景噪声下滚动轴承早期微弱故障特征难以提取以及奇异谱分解方法分解的分量仍然包含噪声的问题，提出了一种奇异谱分解（Singular spectrum decomposition，SSD）和最大循环平稳盲解卷积（Maximum cyclostationarity blind deconvolution，CYCBD）相结合的滚动轴承微弱故障特征提取方法。由SSD方法将轴承振动信号自适应地分解为从高频到低频的奇异谱分量；根据分量峭度最大原则，筛选出最佳分量；再利用CYCBD对最佳分量后处理进一步降噪；进而对降噪后的信号进行Hilbert包络解调分析，得到故障特征频率。仿真和实验分析表明，该方法能有效提取滚动轴承早期微弱故障特征。

Abstract

Aiming at the problem of early weak fault features of rolling bearings are difficult to be extracted under strong background noise and the components decomposed by the singular spectral decomposition method still contain noise，a method of extracting the weak fault features of rolling bearing based on the combination of singular spectrum decomposition （SSD） and maximum cyclostationarity blind deconvolution （CYCBD） is proposed. The SSD method is used to adaptively decompose the bearing vibration signal into high-frequency to low-frequency singular spectral components. The best component is selected according to the principle of maximum component kurtosis. The best component is used in CYCBD post-processing for further noise reduction. Furthermore，the noise reduced signal is analyzed by Hilbert envelope demodulation to obtain the fault characteristic frequency. Simulation and experimental analysis show that this method can extract early weak fault features of rolling bearings effectively.

关键词

滚动轴承奇异谱分解最大2阶循环平稳盲解卷积微弱故障特征提取

Keywords

Rolling bearingSingular spectrum decompositionMaximum second-order cyclostationarity blind deconvolutionWeak faultFeature extraction

references

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.

GU Ran，CHEN Jie，HONG Rongjing，et al.Weak fault diagnosis of rolling bearings based on improved adaptive variational modal decomposition［J］.Vibration and Shock，2020，39（8）：1-7.

王朝阁，李宏坤，胡少梁，等.利用参数自适应多点最优最小熵反褶积的行星轮轴承微弱故障特征提取［J］.振动工程学报，2021，34（3）：633-645.

WANG Chaoge，LI Hongkun，HU Shaoliang，et al.Extraction of weak fault features of planetary wheel bearings using parametric adaptive multipoint optimal minimum entropy inverse fold product［J］.Journal of Vibration Engineering，2021，34（3）：633-645.

HUANG W，GAO G，LI N，et al.Time-frequency squeezing and generalized demodulation combined for variable speed bearing fault diagnosis［J］.IEEE Transactions on Instrumentation and Measurement，2019，68（8）：2819-2829.

JIANG Y，ZHU H，LI Z，A new compound faults detection method for rolling bearings based on empirical wavelet transform and chaotic oscillator［J］.Chaos，Solitons and Fractals 2016，89：8-19.

ATIK F，KENG N W，LIM M H.Noise eliminated ensemble empirical mode decomposition for bearing fault diagnosis［J］.Journal of Vibration Engineering and Technologies，2021（8）：2523-3920.

谢小正，李俊，赵荣珍，等.SVD-LMD联合降噪和TEO的滚动轴承故障诊断［J］.机械传动，2021，45（6）：104-112.

XIE Xiaozheng，LI Jun，ZHAO Rongzhen，et al.Combined SVD-LMD noise reduction and TEO for rolling bearing fault diagnosis［J］.Journal of Mechanical Transmission，2021，45（6）：104-112.

JIANG X，WANG J，SHI J，et al.A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines［J］.Mechanical Systems and Signal Processing，2019116：668-692.

刘柯欣，孙虎儿，梁富旺.基于TVF-EMD和TEO的滚动轴承微弱故障特征提取［J］.机械传动，2021，45（3）：165-170.

LIU Kexin，SUN Huer，LIANG Fuwang.Weak fault feature extraction of rolling bearings based on TVF-EMD and TEO［J］.Journal of Mechanical Transmission，2021，45（3）：165-170.

杨超，赵荣珍，孙泽金.基于SVD-MEEMD与Teager能量谱的滚动轴承微弱故障特征提取［J］.噪声与振动控制，2020，40（4）：92-97.

YANG Chao，ZHAO Rongzhen，SUN Zejin.Faint fault feature extraction of rolling bearings based on SVD-MEEMD and Teager energy spectrum［J］.Noise and Vibration Control，2020，40（4）：92-97.

BONIZZI P，KAREL J M H，MESTE O，et al.Singular spectrum decomposition：a new method for time series decomposition［J］.Advances in Adaptive Data Analysis，2014，6（4）：1-34.

唐贵基，姜乐，王晓龙.基于SSD和MPE的滚动轴承故障诊断方法［J］.组合机床与自动化加工技术，2020（3）：70-73.

TANG Guiji，JIANG Le，WANG Xiaolong.Fault diagnosis method of rolling bearing based on SSD and MPE［J］.Combined Machine Tools and Automatic Machining Technology，2020（3）：70-73.

唐贵基，李楠楠，王晓龙，等.基于SSD和Teager能量算子的滚动轴承故障诊断方法［J］.河南理工大学学报（自然科学版），2020，39（4）：82-87.

TANG Guiji，LI Nannan，WANG Xiaolong，et al.A rolling bearing fault diagnosis method based on SSD and Teager energy operator［J］.Journal of Henan University of Technology （Natural Science Edition），2020，39（4）：82-87.

胥永刚，张志新，马朝永，等.改进奇异谱分解及其在轴承故障诊断中的应用［J］.振动工程学报，2019，32（3）：540-547.

XU Yonggang，ZHANG Zhixin，MA Chaoyong，et al.Improved singular spectrum decomposition and its application in bearing fault diagnosis［J］.Journal of Vibration Engineering，2019，32（3）：540-547.

BUZZONI M，ANTONI J，DELIA G.Blind deconvolution based on cyclostationarity maximization and its application to fault identification［J］.Journal of Sound and Vibration，2018（432）：569-601.

WANG Z J，ZHOU J，DU W H，et al.Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution［J］.Mechanical Systems and Signal Processing，2021，162（5/6/7/8）：1096-1216.

Case Western Reserve University Bearing Data Center.Seeded fault test date［DB/OL］［2021-09-06］.http：//csegroups.case.edu/bearing‐datacenter/homehttp://csegroups.case.edu/bearing‐datacenter/home．

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