基于TVF-EMD和TEO的滚动轴承微弱故障特征提取

刘柯欣; 孙虎儿; 梁富旺

doi:10.16578/j.issn.1004.2539.2021.03.027

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基于TVF-EMD和TEO的滚动轴承微弱故障特征提取

开发应用 | 更新时间：2022-11-10

- 基于TVF-EMD和TEO的滚动轴承微弱故障特征提取
- Feature Extraction of Weak Fault of Rolling Bearing based on TVF-EMD and TEO
- 机械传动 2021年45卷第3期页码：165-170
- 作者机构：
  
  1.中北大学机械工程学院，山西太原 030051
- 作者简介：
  
  刘柯欣（1996— ），女，山西运城人，硕士研究生，研究方向为故障诊断、机电一体化。
  孙虎儿（1972— ），男，山西寿阳人，博士，副教授，研究方向为机械设备状态监测与故障诊断、摩擦科学与工程。
- 基金信息：
  
  山西省自然科学基金(201801D121186);智能化混凝土布料系统关键技术(18DZ1205602)
- DOI：10.16578/j.issn.1004.2539.2021.03.027
  中图分类号：
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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(03):165-170.

Liu Kexin,Sun Huer,Liang Fuwang.Feature Extraction of Weak Fault of Rolling Bearing based on TVF-EMD and TEO[J].Journal of Mechanical Transmission,2021,45(03):165-170.
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(03):165-170. DOI： 10.16578/j.issn.1004.2539.2021.03.027.

Liu Kexin,Sun Huer,Liang Fuwang.Feature Extraction of Weak Fault of Rolling Bearing based on TVF-EMD and TEO[J].Journal of Mechanical Transmission,2021,45(03):165-170. DOI： 10.16578/j.issn.1004.2539.2021.03.027.

摘要

针对旋转机械转子振动信号通常伴随着强噪声，难以提取其有效信息的问题，提出一种基于时变滤波经验模态分解（Time varying filtering based empirical mode decomposition，TVF-EMD）和Teager能量算子（Teager energy operator，TEO）相结合的故障特征提取方法。首先，用TVF-EMD方法自适应地分解轴承振动信号，以获得一组本征模态函数（Intrinsic mode functions，IMFs）；然后，对分解结果进行峭度计算，并根据峭度最大准则选出峰度值最高的敏感分量；进而，利用Teager能量算子对选定的敏感分量进行解调处理，通过观察明显的周期性故障特征频率来实现轴承微弱故障特征提取。进行了仿真和实验，结果证明，该方法能有效实现轴承微弱故障的诊断。

Abstract

Aiming at the problem that the vibration signals for rotating machinery rotors are usually accompanied by strong noise， it is difficult to extract its effective information. A method of fault feature extraction based on time-varying filter empirical mode decomposition （TVF-EMD） and Teager energy operator （TEO） is proposed. Firstly， the TVF-EMD method is used to adaptively decompose the bearing vibration signal to obtain a set of intrinsic modal functions. Then， the kurtosis calculation is performed on the decomposition result， and the sensitive component is selected with the highest kurtosis value according to the maximum kurtosis criterion. Furthermore， the Teager energy operator is used to demodulate the selected sensitive components， and the weak fault feature extraction of the bearing is realized by observing the obvious periodic fault feature frequency. Simulations and experiments are carried out， and the results prove that this method can effectively diagnose the weak faults of bearings.

关键词

滚动轴承微弱故障时变滤波经验模态分解（TVF-EMD）Teager能量算子

Keywords

Rolling bearingWeak faultTVF-EMDTEO

references

别锋锋，谷晟，庞明军，等.基于CEEMDAN-DRT的滚动轴承故障诊断方法研究［J］.机械传动，2020，44（4）：158-164.

BIE Fengfeng，GU Sheng，PANG Mingjun，et al.Research based on CEEMDAN-DRT fault diagnosis method of rolling bearing［J］.Journal of Mechanical Transmission，2020，44（4）：158-164.

MA P，ZHANG H L，FAN W H，et al.A diagnosis framework based on domain adaptation for bearing fault diagnosis across diverse domains［J］.ISA Transactions，2018，99：465-78.

WANG R X，JIANG H K，LI X Q，et al.A reinforcement neural architecture search method for rolling bearing fault diagnosis［J］.Measurement，2020，154：107417.

张泽宇，慕世铭，高刚毅.探讨机械零件的摩擦磨损与防护［J］.内燃机与配件，2020（3）：130-131.

ZHANG Zeyu，MU Shiming，GAO Gangyi.Discussion on friction and wear of mechanical parts and protection［J］.Internal Combustion Engine and Accessories，2020（3）：130-131.

HUANG N E，SHEN Z，LONG S R，et al.The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis［J］.Proceedings A，1998，54（1971）：903-995.

戴邵武，郑百东，戴洪德，等.基于EMD-LWT的光纤陀螺阈值去噪［J］.光电工程，2019，46（5）：40-46.

DAI Shaowu，ZHENG Baidong，DAI Hongde，et al.Based on EMD-LWT threshold denoising of fiber optic gyroscope［J］.Optoelectronic Engineering，2019，46（5）：40-46.

ZHENG J D，CHENG J S，YANG Y.Partly ensemble empirical mode decomposition：An improved noise-assisted method for eliminating mode mixing［J］.Signal Processing，2014，96：362-374.

LI J M，WANG H，ZHANG J F，et al.Impact fault detection of gearbox based on variational mode decomposition and coupled underdamped stochastic resonance［J］.Isa Transactions.2019，95：320-329.

徐俊祖，王晓东，吴建德，等.基于ENEMD与Teager能量算子的轴承早期微弱故障特征提取研究［J］.电子测量与仪器学报，2019，33（3）：85-92.

XU Junzu，WANG Xiaodong，WU Jiande，et al.Based on ENEMD and teager energy operators research on feature extraction of early weak faults in bearings［J］.Journal of Electronic Measurement and Instrument，2019，33（3）：85-92.

MA J，WU J D，WANG X D.Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator［J］.ISA Transactions，2018，80：297-311.

GAO Q，TANG H S，XIANG J W，et al.A walsh transform-based teager energy operator demodulation method to detect faults in axial piston pumps［J］.Measurement，2019，134：293-306.

周洋，向阳，黄陈哲，等.基于VMD-SVD能量标准谱-Teager能量算子的轴承故障诊断方法［J］.噪声与振动控制，2019，39（4）：212-217.

ZHOU Yang，XIANG Yang，HUANG Chenzhe，et al.Based on VMD-SVD energystandard spectrum and teager energy operator bearing fault diagnosis method［J］.Noise and Vibration Control，2019，39（4）：212-217.

任学平，李攀，乔海懋.基于CEEMD和Teager能量算子的滚动轴承故障诊断研究［J］.机械设计与制造，2019（8）：16-18.

REN Xueping，LI Pan，QIAO Haimao.Research on fault diagnosis of rolling bearing based on CEEMD and teager energy operator［J］.Mechanical Design and Manufacturing，2019（8）：16-18.

Case Western Reserve University Bearing Data Center.Seeded fault test date［DB/OL］.［2020-04-15］.http：//csegroups.case.edu/bearingdatacenter/homehttp://csegroups.case.edu/bearingdatacenter/home．

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