Hybrid wavelet-SG-EEMD algorithm and its application in chaotic de-noising

被引：0
作者：
Wei, Xiu-Lei [1 ]
Lin, Rui-Lin [1 ]
Liu, Shu-Yong [1 ]
Wang, Qiang [1 ]
机构：
[1] College of Power Engineering, Naval University of Engineering, Wuhan
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2015年 / 34卷 / 17期
关键词：
Chaotic signal; EEMD; SG algorithm; Wavelet transformation;
D O I：
10.13465/j.cnki.jvs.2015.17.017
中图分类号：
学科分类号：
摘要：
Since some or all of spectral bands of chaotic signals and those of noise overlap, chaotic signals cannot be extracted effectively from strong disturbances with a single denoising method. Here, the hybrid wavelet-SG-EEMD algorithm was proposed. With the proposed algorithm, the wavelet-Savitzky-Golay(wavelet-SG) algorithm was taken as the pre-filter element of the ensemble empirical mode decomposition(EEMD) in order to reduce the effects of random white noise and local strong disturbances, and then the hydrid algorithm was combined with the characteristics restraining mode mixing of EEMD to extract the chaotic signals from complex and strong disturbances effectively. The implementation of the hybrid filtering algorithm was evaluate with Lorenz time series. Finally, the method was applied in 2-DOF chaotic vibration signals, and the results showed that the strong noise can be filtered normally. ©, 2015, Chinese Vibration Engineering Society. All right reserved.
引用
收藏
页码：100 / 104and110
相关论文
共 15 条
  • [1] Alexander L.F., Robin J.E., Control of chaos: Methods and applications in engineering, Annual Reviews in Control, 29, 1, pp. 33-56, (2005)
  • [2] Kostelich E.J., Schreiber T., Noise reduction in chaotic time series: A survey of common methods, Physical Review E, 48, 3, pp. 1752-1763, (1993)
  • [3] Elshorbagy A., Simonovic S.P., Panu U.S., Noise reduction in chaotic hydrologic time series: facts and doubts, Journal of Hydrology, 256, 3-4, pp. 147-165, (2002)
  • [4] 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 of the Royal Society of London Series A-Mathematical Physical and Engineering Science, 454, pp. 903-995, (1998)
  • [5] Leng J.-C., Liu S.-L., Noise reduction of chaotic signal based on EMD method, Journal of Vibration and Shock, 25, pp. 326-328, (2006)
  • [6] Boudraa A.O., Cexue J.C., De-noising via empirical mode decomposition, Proceedings of the IEEE International Symposium on Control Communications and Signal Processing, pp. 4-8, (2006)
  • [7] Bo L., Liu X.-F., Qin S.-R., Hybrid wavelet-morphology-EMD analysis and its application, Journal of Vibration and Shock, 27, 5, pp. 1-4, (2008)
  • [8] Chen R.-X., Tang B.-P., Ma J.-H., Adaptive de-noising method based on ensemble empirical mode decomposition for vibration signal, Journal of Vibration and Shock, 31, 15, pp. 82-86, (2012)
  • [9] Wu Z., Huang N.E., A study of the characteristics of white noise using the empirical mode decomposition method, Proc. R. London A, 460, pp. 1597-1611, (2004)
  • [10] Zhang Y.-Y., Li S.-M., Hu Y.-X., Et al., Improvement of LMS method and its application combined with EEMD in vehicle vibration signal denoising, Journal of Vibration and Shock, 32, 20, pp. 61-66, (2013)
12