Time-frequency Analysis of Non-Stationary Signal Based on NDSST

被引:0
|
作者
Hao G. [1 ,2 ,3 ]
Li F. [1 ]
Bai Y. [1 ,3 ]
Wang W. [1 ]
机构
[1] School of Mechanical Engineering and Electronic Information, China University of Geosciences(Wuhan), Wuhan
[2] Department of Mathematics, Duke University, Durham, 27708, NC
[3] Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems, Wuhan
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Metal rupture signal; NDSST algorithm; Robustness; SNR; Time-frequency analysis;
D O I
10.13203/j.whugis20170271
中图分类号
学科分类号
摘要
The time-frequency analysis methods are used to study the time-frequency distribution characteristics of non-stationary signal with noise. De-shape SST (De-shape synchrosqueezing transform) algorithm has good time-frequency performance. However, the anti-noise performance and robustness of the algorithm still need to be improved. A De-shape SST algorithm based on nonlinear matching pursuit decomposition (NDSST) is proposed. The sparse reconstruction of non-stationary signals is carried out via the excellent reconstruction feature of NMP, and then the time-frequency analysis by De-shape SST is performed. The approach in this article can improve the capability of noise reduction and robustness of the algorithm. Meanwhile, an accurate time-frequency aggregation is retained. The numerical simulation results show that the NDSST algorithm can obtain the time-frequency representation with high concentration for single frequency, variable frequency, linear frequency modulation and combined frequency conversion signal. And it has excellent anti-noise property in the low signal noise ratio (SNR) conditions. In the analysis and applications of metal rupture signal, the NDSST algorithm can clearly get the time and instantaneous frequency range of metal rupture. It provides a threshold value for setting up monitoring transducer in engineering practice. © 2019, Editorial Department of Wuhan University of Technology. All right reserved.
引用
收藏
页码:941 / 948
页数:7
相关论文
共 24 条
  • [1] Guo H., Cong P., Application of Hilbert-Huang Transform in Dam Monitoring Data Analysis, Geomatics and Information Science of Wuhan University, 32, 9, pp. 774-777, (2007)
  • [2] Boashash B., Chapter 14-Time-Frequency Methods in Radar, Sonar & Acoustics, pp. 577-625, (2003)
  • [3] Shi J., Gao H., Zhou L., Et al., Adaptive Multi-target Detection Based on SMVF for HF Radar, Geomatics and Information Science of Wuhan University, 39, 11, pp. 1304-1309, (2014)
  • [4] Liu D., Study on On-Line Nondestructive Testing of Roof Rupture of Six-side Top Press, (2015)
  • [5] Gabor D., Theory of Communication, Journal of the Institute of Electrical Engineers of Japan, 93, pp. 429-457, (1946)
  • [6] Daubechies I., Ten Lectures on Wavelets, (1992)
  • [7] Boashash B., Black P., An Efficient Real-time Implementation of the Wigner-Ville Distribution, IEEE Transactions on Acoustics Speech & Signal Processing, 35, 11, pp. 1611-1618, (1987)
  • [8] Stockwell R.G., Mansinha L., Lowe R.P., Localization of the Complex Spectrum: The S Transform, IEEE Transactions on Signal Processing, 44, 4, pp. 998-1001, (1996)
  • [9] Huang N.E., Shen Z., Long S.R., Et al., The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis, Proceedings Mathematical Physical & Engineering Sciences, 454, 1971, pp. 903-995, (1998)
  • [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)