Application of SVD Based on Correlated Singular Value Ratio in Bearing Fault Diagnosis

被引：0
|
作者：
Li H. [1 ,2 ]
Liu T. [2 ]
Wu X. [2 ,3 ]
Li S. [1 ]
机构：
[1] State Key Laboratory of Public Big Data, Guizhou University, Guiyang
[2] Yunnan Provincial Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology, Kunming University of Science and Technology, Kunming
[3] Yunnan Vocational College of Mechanical and Electrical Technology, Kunming
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2021年 / 57卷 / 21期
关键词：
Cross-correlation coefficient; Hankel matrix structure; Reconstructed component determination; Rolling bearing; Singular value decomposition; Singular value ratio;
D O I：
10.3901/JME.2021.21.138
中图分类号：
学科分类号：
摘要：
The SVD method based on Hankel matrix is widely used in signal processing and fault diagnosis. Its noise reduction performance is affected by the selected reconstruction component, the structure of the Hankel matrix, and the number of points of the analysis data. Based on this, a systematic research is carried out, and the SVD based on correlated singular value ratio (C-SVR SVD) is proposed, and successfully applied to bearing fault diagnosis. First, for the problem of determining the reconstruction components of SVD, a method combining singular value ratio (SVR) and cross-correlation coefficient is proposed; secondly, the structure of Hankel matrix is studied, and a structure optimization method based on SVR and kurtosis indicator is proposed. The structure optimization method of the indicator. Then, the number of analyzed data points was analyzed and discussed, and constraints were given. Finally, the C-SVR SVD method is applied to the analysis of the bearing fault simulation signal and the actual bearing fault signal, which verifies the effectiveness and superiority of the C-SVR SVD method. © 2021 Journal of Mechanical Engineering.
引用
收藏
页码：138 / 149
页数：11
相关论文
共 20 条
  • [1] WANG Huaqing, HOU Wei, TANG Gang, Et al., Fault detection enhancement in rolling element bearings via peak-based multi-scale decomposition and envelope demodulation, Mathematical Problems in Engineering, 1, pp. 135-142, (2014)
  • [2] CHEN Xianglong, FENG Fuzhou, ZHANG Bingzhi, Et al., Rolling bearing fault diagnosis with optimal resonant frequency band demodulation based on squared envelope spectral correlated kurtosis, Journal of Mechanical Engineering, 54, 21, pp. 90-100, (2018)
  • [3] LI Hua, WU Xing, LIU Tao, Et al., Application of variational mode decomposition and improved adaptive resonance technology in bearing fault feature extraction, Journal of Vibration Engineering, 31, 4, pp. 718-726, (2018)
  • [4] LI Hua, WU Xing, LIU Tao, Et al., Research on bearing fault feature extraction based on information entropy optimized variational modal decomposition, Journal of Vibration and Shock, 37, 23, pp. 219-225, (2018)
  • [5] CUI Lingli, WANG Xin, WANG Huaqing, Et al., Feature extraction of bearing fault based on improved switching Kalman filtering, Journal of Mechanical Engineering, 55, 7, pp. 44-51, (2019)
  • [6] GOLAFSHAN R, SANLITURK K Y., SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults, Mechanical Systems and Signal Processing, 70-71, 3, pp. 36-50, (2016)
  • [7] LAUB A J., The singular value decomposition: Its computation and some applications, Automatic Control IEEE Transactions on, 25, 2, pp. 164-176, (1980)
  • [8] KANJIL P P, PALIT S, SAHA G., Fetal ECG extraction from single-channel maternal ECG using singular value decomposition, Biomedical Engineering, IEEE Transactions on, 44, 1, pp. 51-59, (1997)
  • [9] YANG Wenxian, REN Xingmin, JIANG Jiesheng, On improving the effectiveness of the new noise reduction technique based on singularity spectrum, Journal of Northwestern Polytechnical University, 19, 3, pp. 368-371, (2001)
  • [10] ZHAO Xuezhi, YE Bangyan, CHEN Tongjian, Difference spectrum theory of singular value and its application to the fault diagnosis of headstock of lathe, Journal of Mechanical Engineering, 46, 1, pp. 100-108, (2010)
12