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
Vibration Characteristic Analysis and Feature Extraction of Bearing Coupling Fault based on Sparse Representation
- 机械传动 2020年44卷第10期 页码：38-43
- 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：
巩晓赟（1981— ），女，河南郑州人，博士，副教授，硕士研究生导师，主要研究方向为设备状态监测与故障诊断。
张伟业（1995— ），男，河南安阳人，硕士研究生，主要研究方向为转子系统的状态监测与故障诊断。
- 基金信息：国家自然科学基金(U1804141);河南省科技攻关项目(182102410052);河南省教育厅青年骨干教师项目(2018GGJS091)
DOI：10.16578/j.issn.1004.2539.2020.10.006
中图分类号：
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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
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