Fault feature extraction method of rolling bearings based on VMD and manifold learning

被引:0
|
作者
Qi X. [1 ]
Ye X. [1 ]
Cai J. [1 ]
Zheng J. [1 ]
Pan Z. [1 ]
Zhang X. [1 ]
机构
[1] School of Mechanical Engineering, Anhui University of Technology, Ma'anshan
来源
| 2018年 / Chinese Vibration Engineering Society卷 / 37期
关键词
Cylindrical roller bearing; Fault diagnosis; Local tangent space alignment (LTSA) algorithm; Manifold learning; Variational mode decomposition (VMD);
D O I
10.13465/j.cnki.jvs.2018.23.019
中图分类号
学科分类号
摘要
A method for early fault diagnosis of rolling bearings based on the variational mode decomposition (VMD) combined with the local tangent space alignment (LTSA) algorithm was proposed. Firstly, the VMD algorithm was used to decompose vibration signals of a rolling bearing under different operational conditions, and several components most correlated to the original signal were screened out by solving the instantaneous frequency mean and plotting the feature curve. Then, the time domain index of the effective modal component and the frequency domain index formed with the wavelet packet frequency band decomposition energy were extracted. After these two indexes were combined to primarily extract higher dimensional fault features, LTSA was used to extract fault features again. Finally, the extracted fault features were inputted into a K-means classifier to recognize fault types. Through the contrastive test analysis of cylindrical roller bearings' fault diagnosis, it was shown that compared with the method of time frequency features extraction+LTSA and the method of EMD+LTSA, the method of VMD+LTSA has more advantages in classification effect and recognition accuracy; the LTSA algorithm has the best sensitivity to fault features after dimension reduction compared to the algorithms of PCA, LPP, LE, ISOMAP and LLE; the proposed method has a certain superiority in the fault diagnosis of cylindrical roller bearings. © 2018, Editorial Office of Journal of Vibration and Shock. All right reserved.
引用
收藏
页码:133 / 140
页数:7
相关论文
共 15 条
  • [1] Zheng J., Cheng J., Yang Y., A rolling bearing fault diagnosis method based on LCD and permutation entropy, Journal of Vibration and Shock, 34, 5, pp. 802-806, (2014)
  • [2] Zhu K., Song X., Xue D., Incipient fault diagnosis of roller bearings using empirical mode decomposition and correlation coefficient, Journal of Vibroengineering, 15, 2, pp. 597-603, (2013)
  • [3] Zhang S., Zhai X., Dong X., Et al., Application of EMD and Duffing oscillator to fault line detection in un-effectively grounded system, Proceedings of the CSEE, 33, 10, pp. 161-167, (2013)
  • [4] Chen Y., Gao P., He T., Et al., Roller bearing comprehensive fault diagnosis based on LMD, Journal of Vibration and Shock, 31, 3, pp. 73-78, (2012)
  • [5] Wang Y., He Z., Zi Y., A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis, Journal of Vibration & Acoustics, 132, 2, pp. 613-624, (2010)
  • [6] Cheng J., Zhang K., Yang Y., Et al., Comparison of local mean decomposition and empirical mode decomposition, Journal of Vibration and Shock, 28, 5, pp. 13-16, (2009)
  • [7] Liu C., Wu Y., Zhen C., Rolling bearing fault diagnosis based on variational mode decomposition and fuzzy C means clustering, Proceedings of the CSEE, 35, 13, pp. 3358-3365, (2015)
  • [8] Dragomiretskiy K., Zosso D., Variational mode decomposition, IEEE Transactions on Signal Processing, 62, 3, pp. 531-544, (2014)
  • [9] An X., Tang Y., Application of variational mode decomposition energy distribution to bearing fault diagnosis in a wind turbine, Transactions of the Institute of Measurement & Control, 5, 2, pp. 753-772, (2016)
  • [10] Wang Y., Markert R., Xiang J., Et al., Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system, Mechanical Systems & Signal Processing, 60-61, pp. 243-251, (2015)