Impact feature extraction from rolling bearing fault signal by synchrosqueezed S-transform

被引：0
作者：
Pan G.-Y. [1 ]
Li S.-M. [1 ]
An Z.-H. [1 ]
Zeng Y. [2 ]
机构：
[1] College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing
来源：
Zhendong Gongcheng Xuebao/Journal of Vibration Engineering | 2020年 / 33卷 / 02期
关键词：
Fault diagnosis; Impact feature; Rolling bearing; Synchrosqueezed S-transform; Synchrosqueezed wavelet transform;
D O I：
10.16385/j.cnki.issn.1004-4523.2020.02.024
中图分类号：
学科分类号：
摘要：
Bearing is the core component of many transmission structures, of which the damage will directly affect the normal operation of the mechanism. In order to extract the impact features from the rolling bearing fault signal, which is important for bearing fault diagnosis, a signal processing method of synchrosqueezed S-transform (SSST) is introduced. Based on synchrosqueezed wavelet transform (SST) and S transform, the definition formulas of synchrosqueezed S-transform is derived. Cosine-frequency modulation simulation signals and shock simulation signals are analyzed using S transform, SST and SSST. The results show that the SSST preserves good time-frequency resolution and time-frequency concentration over the entire frequency band of the analyzed signal with better performance than the S transform and SST. A set of physical rolling ball bearing fault vibration signals is analyzed. The results show that the SSST can conveniently and effectively extract the periodical impact features from the vibration signals. © 2020, Nanjing Univ. of Aeronautics an Astronautics. All right reserved.
引用
收藏
页码：433 / 440
页数：7
相关论文
共 19 条
  • [1] Guo Y., Wei Y., Zhou X., Et al., Impact feature extraction from rolling bearing fault signal by S transform, Journal of Vibration, Measurement & Diagnosis, 34, 5, (2014)
  • [2] Ding K., Huang Z., Lin H., A weak fault diagnosis method for rolling element bearings based on Morlet wavelet and spectral kurtosis, Journal of Vibration Engineering, 27, 1, pp. 128-135, (2014)
  • [3] Stockwell G.R., Mansinha L., Lowe R.P., Localization of the complex spectrum: The S-transform, IEEE Transactions on Signal Processing, 44, 4, pp. 998-1001, (2002)
  • [4] Wang Q., Gao J., Liu N., Et al., High-resolution seismic time-frequency analysis using the synchrosqueezing generalized S-transform, IEEE Geoscience & Remote Sensing Letters, 99, pp. 1-5, (2018)
  • [5] Zheng C.-L., Wang B.-S., Applications of S transform in seismic data processing, Progress in Geophysics, 30, 4, pp. 1580-1591, (2015)
  • [6] Qu Z.-D., Wu W., He R.-Z., Et al., Soft threshold filter based on S-transform and its application to data processing of deep seismic reflection, Chinese Journal of Geophysics, 58, 9, pp. 3157-3168, (2015)
  • [7] Wu Y., Tang Q., Teng Z., Et al., Feature extraction method of power quality disturbance signals based on modified S-transform, Proceedings of the CSEE, 36, 10, pp. 2682-2689, (2016)
  • [8] Li W., Lin L., Shan W., Bearing fault diagnosis based on generalized S-transform and two-directional 2DPCA, Journal of Vibration, Measurement & Diagnosis, 35, 3, pp. 499-506, (2015)
  • [9] Liu H., Zhang J., Huang Z., Time-frequency analysis of seismic data using synchrosqueezing S-transform, Oil Geophysical Prospecting, 52, 4, (2017)
  • [10] Daubechies I., Lu J., Wu H.T., Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool, Applied & Computational Harmonic Analysis, 30, 2, pp. 243-261, (2011)
12