Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

2.10

CiteScore

Jingzong Yang This email address is being protected from spambots. You need JavaScript enabled to view it.1, Xiaodong Wang2,3, Jiande Wu2,3 and Zao Feng2,3

1School of Information, Baoshan University, Baoshan, Yunnan 678000, P.R. China
2Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P.R. China
3Engineering Research Center for Mineral Pipeline Transportation of Yunnan Province, Kunming 650500, P.R. China


 

Received: January 10, 2018
Accepted: October 28, 2018
Publication Date: March 1, 2019

Download Citation: ||https://doi.org/10.6180/jase.201903_22(1).0001  

ABSTRACT


In order to separate the noise sources of pipeline blockage signals from complex single channel signals effectively, a noise reduction method based on complete ensemble empirical mode decomposition (CEEMD) and robust independent component analysis (RobustICA) is proposed. First, the intrinsic mode function (IMF) obtained by CEEMD is analyzed, and then the related IMF components are reorganized and a virtual channel is constructed. Finally, the virtual channel and the original signal are input as the blind source separation signal, and RobustICA is used to separate the signal source and noise, so as to achieve the purpose of reducing the noise. Through the analysis of the noise reduction effect of simulation signal and pipeline acoustic blockage detection signal, the results show that proposed method is superior to FastICA and EEMD based denoising method in denoising effect and performance index.


Keywords: CEEMD, RobustICA, Noise Reduction, Pipeline


REFERENCES


  1. [1] Sinha, S. L., S. K. Dewangan, and A. Sharma (2015) A review on particulate slurry erosive wear of industrial materials: in context with pipeline transportation of mineralâslurry, ParticulateScience&Technology 35(1), 103 118. doi: 10.1080/02726351.2015.1131792
  2. [2] Jiang, S. C., B. R. Cheng, and C. L. Wen (2011) The application prospect of pipeline transportation in metallurgicalmine,Conservation &Utilizationof Mineral Resources (5), 114 117. doi: 10.13779/j.cnki.issn 1001-0076.2011.z1.026
  3. [3] Datta, S., and S. Sarkar (2016) A review on different pipeline fault detection methods, Journal of Loss Prevention in the Process Industries 41(2016), 97106. doi: 10.1016/j.jlp.2016.03.010
  4. [4] Liu, S. H., and Y. Zhang (2015) Signal denoising methods based on wavelet analysis, Mechanical Engineering & Automation (1), 84 88. (In Chinese)
  5. [5] Coifman, R. R. (1992) Wavelet analysis and signal processing, Acta Petrologica Et Mineralogica 9(3), 765 780.
  6. [6] Yeh, J. R., J. S. Shieh, and N. E. Huang (2010) Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method, Advances in Adaptive Data Analysis 2(2), 135 156. doi: 10.1142/S1793536910000422
  7. [7] Torres, M. E., M. A. Colominas, G. Schlotthauer, et al. (2011) A complete ensemble empirical mode decomposition with adaptive noise, IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 4144 4147.
  8. [8] Jiao, W., Z. Li, D. Wang, et al. (2014) A method for wavelet threshold denoising of seismic data based on CEEMD, Geophysical Prospecting for Petroleum 53(2), 164 172.
  9. [9] Zhou, T. T., X. M. Zhu, W. C. Peng, et al. (2015) A waveletthreshold denoising method for faultdatabased on CEEMD and permutation entropy, Journal of Vibration & Shock 34(23), 207 211. doi: 10.13465/j. cnki.jvs.2015.23.036
  10. [10] Li, J., C. Liu, Z. Zeng, et al. (2015) GPR signal denoising and target extraction with the CEEMD method, IEEE Geoscience & Remote Sensing Letters 12(8), 1615 1619. doi: 10.1117/12.2050432
  11. [11] Huang, N. E., Z. Shen, S. R. Long, et al. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings Mathematical Physical &Engineering Sciences 454(1971), 903 995. doi: 10.1098/rspa. 1998.0193
  12. [12] Zarzoso, V., and P. Comon (2010) Robust independent component analysis by iterative maximization of the kurtosis contrast with algebraic optimal step size. IEEE Transactions on Neural Networks 21(2), 248 261. doi: 10.1109/TNN.2009.2035920
  13. [13] Blanco-Velasco,M.,B.Weng, and K. E.Barner (2008) ECG signal denoising and baseline wander correction based on the empirical mode decomposition, Computers in Biology &Medicine 38(1), 1 13. doi: 10.1016/j. compbiomed.2007.06.003
  14. [14] Wu, Z.H., and Norden E.Huang (2005) Ensembleempirical mode decomposition: a noise-assisted data analysis method, Advances in Adaptive Data Analysis 1(1), 1 41. doi: 10.1142/S1793536909000047
  15. [15] Hyvärinen, A., J. Hurri, and P. O. Hoyer (2009) Independent Component Analysis, Natural Image Statistics, Springer London, 529 529.
  16. [16] Sun, Y., and S. Su (2015) Research on noise reduction of internet of things based on power line using independent component analysis, Journal of Xian University of Posts & Telecommunications 2(6), 23 27. doi: 0.13682/j.issn.2095-6533.2015.06.006 (In Chinese)
  17. [17] Boudraa, A. O., and J. C. Cexus (2007) EMD-based signalfiltering,IEEE Transactions on Instrumentation & Measurement56(6), 2196 2202. doi: 10.1109/TIM. 2007.907967
  18. [18] Wei, X., R. Lin, S. Liu, et al. (2016) Improved EEMD denoising method based on singular value decomposition for the chaotic signal, Shock and Vibration 2016(12), 1 14. doi: 10.1155/2016/7641027
  19. [19] Cheng, W. D., and D. Z. Zhao (2016) EMD soft-thresholding denoising algorithm for rolling element bearing rotational frequency estimation, Journal of Zhejiang University (Engineering Science) 50(3), 428 435. doi: 10.3785/j.issn.1008-973X.2016.03.005 (In Chinese)
  20. [20] Jiang, S. F., Z. G. Chen, Q. H. Shen, et al. (2016) Damage detection and locating based on EEMD-Fast ICA, Journal of Vibration & Shock 35(1), 203 209. doi: 10. 13465/j.cnki.jvs.2016.01.032 (In Chinese).
  21. [21] Bi, F., L. Li, J. Zhang, et al. (2015) Source identification of gasoline engine noise based on continuous wavelet transform and EEMD–RobustICA, Applied Acoustics 100, 34 42. doi: org/10.1016/j.apacoust. 2015.07.007


    



 

2.1
2023CiteScore
 
 
69th percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.