Vibration Characteristic Analysis and Feature Extraction of Bearing Coupling Fault based on Sparse Representation

Xiaoyun Gong; Weiye Zhang; Yongjie Jing; Wenliao Du

doi:10.16578/j.issn.1004.2539.2020.10.006

您当前的位置：

首页 >

文章列表页 >

Vibration Characteristic Analysis and Feature Extraction of Bearing Coupling Fault based on Sparse Representation

Theory·Reserach | 更新时间：2022-11-10

- Vibration Characteristic Analysis and Feature Extraction of Bearing Coupling Fault based on Sparse Representation
- Journal of Mechanical Transmission Vol. 44, Issue 10, Pages: 38-43(2020)
- 作者机构：
  
  1.郑州轻工业大学机电工程学院，河南郑州 450003
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2020.10.006
  CLC：
扫描看全文
巩晓赟,张伟业,敬永杰等.基于稀疏表示的轴承耦合故障振动特性分析及其特征提取[J].机械传动,2020,44(10):38-43.

Gong Xiaoyun,Zhang Weiye,Jing Yongjie,et al.Vibration Characteristic Analysis and Feature Extraction of Bearing Coupling Fault based on Sparse Representation[J].Journal of Mechanical Transmission,2020,44(10):38-43.
巩晓赟,张伟业,敬永杰等.基于稀疏表示的轴承耦合故障振动特性分析及其特征提取[J].机械传动,2020,44(10):38-43. DOI： 10.16578/j.issn.1004.2539.2020.10.006.

Gong Xiaoyun,Zhang Weiye,Jing Yongjie,et al.Vibration Characteristic Analysis and Feature Extraction of Bearing Coupling Fault based on Sparse Representation[J].Journal of Mechanical Transmission,2020,44(10):38-43. DOI： 10.16578/j.issn.1004.2539.2020.10.006.

摘要

稀疏表示在图像处理领域、音频处理领域有着广泛应用，将稀疏表示理论应用在振动信号处理领域可以高效地表示出信号的周期性成分。通过对Adams系统仿真和实测信号的转子耦合故障数据进行稀疏表示，研究了稀疏表示下转子不平衡-轴承耦合故障的振动特性以及故障间相互影响规律。结果表明，耦合故障下轴承故障通常会被淹没，且相较于不平衡故障表现出弱故障特征。针对转子不平衡-轴承耦合故障中轴承故障特征不易提取这一问题，采用基于Gabor原子的稀疏表示方法匹配转子不平衡-轴承耦合故障振动信号中的周期性振动成分，并通过谱峭度算法寻找冲击信号所在频段进行轴承故障特征提取，通过对多组实测信号分析，验证了该方法的有效性。

Abstract

Sparse representation has a wide range of applications in the field of image processing and audio processing. Applying the sparse representation theory to the field of vibration signal processing can efficiently represent the periodic components of the signal. Through the Adams system simulation and measured signal rotor coupling fault data sparse representation， the vibration characteristics of rotor unbalance bearing coupling fault and the law of interaction between faults are studied under the sparse representation. The results show that bearing faults are usually submerged under coupled faults， and it exhibits weak fault characteristics compared to unbalanced faults. Aiming at the problem that the bearing fault feature is difficult to extract in the rotor unbalance bearing coupling fault， using the sparse representation method based on Gabor atom to match the periodic vibration component in the rotor unbalance bearing coupled fault vibration signal， and the spectral kurtosis algorithm is used to find the frequency band where the impact signal is located for bearing fault feature extraction. The effectiveness of the proposed method is verified by analyzing multiple sets of measured signals.

关键词

稀疏表示OMP算法故障诊断振动信号处理耦合故障

Keywords

Sparse representationOMP algorithmFault diagnosisVibration signal processingCoupling fault

references

肖雄，王健翔，张勇军，等.一种用于轴承故障诊断的二维卷积神经网络优化方法［J］.中国电机工程学报，2019，39（15）：4558-4568.

XIAO Xiong，WANG Jianxiang，ZHANG Yongjun，et al.A two-dimensional convolutional neural network optimization method for bearing fault diagnosis［J］.Proceedings of the Chinese Society for Electrical Engineering，2019，39（15）：4558-4568.

NIU Q M，TONG Q B，CAO J，et al.Feature extraction method for condition monitoring of rolling element bearings based on dual-tree complex wavelet packet transform and VMD［J］.Wireless Personal Communications，2018，103（2）：831-845.

井云飞，巩晓赟，张伟业，等.基于振动与电流的感应电机轴承故障诊断系统［J］.设备管理与维修，2018（13）：42-46.

JING Yunfei，GONG Xiaoyun，ZHANG Weiye，et al.Bearing fault diagnosis system of induction motor based on vibration and current［J］.Plant Maintenance Engineering，2018（13）：42-46.

苏建芳，吴钦木.基于小波包分析的电机滚动轴承故障诊断［J］.测控技术，2019，38（4）：64-67.

SU Jianfang，WU Qinmu.Fault dianossis of motor rolling bearing baseed on wavelet packet analysis［J］.Measurement & Control Technology，2019，38（4）：64-67.

王海明，刘永强，廖英英，等.基于EEMD和改进OMP算法的滚动轴承故障诊断［J］.煤矿机械，2019，40（8）：169-172.

WANG Haiming，LIU Yongqiang，LIAO Yingying，et al.Rolling bearing fault diagnosis based on EEMD and improved OMP algorithm［J］.Coal Mine Machinery，2019，40（8）：169-172.

WAN S，PENG B.The fergram：a rolling bearing compound fault diagnosis based on maximal overlap discrete wavelet packet transform and fault energy ratio［J］.Journal of Mechanical Science and Technology，2019，33（1）：157-172.

BARANIUK R G，CANDES E，ELAD M，et al.Applications of sparse representation and compressive sensing［Scanning the Issue］［J］.Proceedings of the IEEE，2010，98（6）：906-909.

周熊.时频重叠通信信号稀疏表示分离方法研究［D］.西安：西安理工大学，2019：11-12.

ZHOU Xiong.Research on spares representation separation method for time-frequency overlapping commun ication signals［D］.Xi'an：Xi'an University of Technology，2019：11-12.

MALLAT S G.Matching pursuits with time-frequency dictionaries［J］.IEEE Transactions on Signal Processing，1993，41（12）：3397-3415.

李雪晴，丁佳静，武雪姣.稀疏度自适应分段正交匹配追踪算法改进［J］.软件工程，2019，22（7）：6-8.

LI Xueqing，DING Jiajing，WU Xuejiao.Improved algorithm for sparsity adaptive stagewise orthogonal matching pursuit［J］.Software Engineering，2019，22（7）：6-8.

LIU Z，WU X J，SHU Z.Sparsity augmented discriminative sparse epresentation for face recognition［J/OL］.Pattern Analysis and Application，2019，22：1527-1535.https：//doi.org/10.1007/s10044-019-00792-5https://doi.org/10.1007/s10044-019-00792-5，2019-03-18.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Gear Fault Diagnosis based on Feature Fusion and Sparse Representation

Bearing Feature Extraction Method Based on the Time Subsequence

A Fault Feature Extraction Method of Rolling Bearings Based on Optimized VMD and UMAP

Research on Fault Diagnosis Method of Planetary Gearboxes Based on DPD-1DCNN

Review on Fault Diagnosis Technology for High-speed EMU Gearboxes

Related Author

No data

Related Institution

School of Mechanical Engineering,Xi’an Shiyou University

School of Information Management, Wuxi Institute of Communications Technology

IOT Engineering College, Wuxi City College of Vocational Technology

State Key Laboratory of Traction Power, Southwest Jiaotong University

School of Rail Transportation, Wuyi University

⁰