Skip to main content
Log in

Removal of noises from electromagnetic radiation of coal or rock with EEMD-adaptive morphological filter

  • Published:
Journal of Coal Science and Engineering (China)

Abstract

The electromagnetic radiation (EMR) signal collected by monitoring system during coal or rock dynamic disaster may be interferred easily by electromagnetic noises in mines. The noises have a direct influence on the recognition and analysis of the EMR signal features during the disaster. With the aim of removing these noises, an ensemble empirical mode decomposition (EEMD) adaptive morphological filter was proposed. From the result of the simulation and the experiment, it is shown that the method can restrain the random noise and white Gaussian noise mixed with EMR signal effectively. The filter is highly useful for improving the robustness of the coal or rock dynamic disaster monitoring system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Dou L M, He X Q, Wang E Y, 2004. Electromagnetic emission technique of monitoring rock burst and its application. Journal of China Coal Society, 29(4): 396–399.

    Google Scholar 

  • Esposito A, D’Andria P, 2003. An adaptive learning algorithm for ECG noise and baseline drift removal. //Neural nets, 14th Italian workshop on neural nets (Apolloni, Bruno, Marinaro, Maria, Tagliaferri, Roberto). Berlin: Springer. 139–147.

    Google Scholar 

  • Frid V I, 1997. Electromagnetic radiation method for rock and gas outburst forecast. Journal of Applied Geophysics, 38(1–2): 97–104.

    Article  Google Scholar 

  • Gao Z H, He F L, Meng J Q, Wang B, 2011. Determine the warning value of drilling cuttings weight coal and rock dynamic disaster through electromagnetic radiation. Journal of China Coal Society, 36(4): 615–618.

    Google Scholar 

  • Hao R J, Lu W X, Chu F L, 2008. Multiscale morphological analysis on fault signals of rolling element bearing. Chinese Journal of Mechanical Engineering, 44(11): 160–165.

    Article  Google Scholar 

  • He Y G, Zhang Y, Zhou H, 2009. Detection of dynamic disturbance voltage using hybrid filter algorithm of morphological filtering and “specific harmonic elimination”. High Voltage Engineering, 35(12): 3093–3098.

    Google Scholar 

  • Hu A J, 2008. Research on the application of Hilbert-Huang transform in vibration signal analysis of rotating machinery. Doctor’s Thesis, North China Electric Power University.

  • Huang N E, Shen Z, Long S R, Wu M C, Shih H H, Zheng Q N, Yen N C, Tung C C, Liu H H, 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear non-stationary time series analysis. Proceeding of Royal Society London A, 454: 903–995.

    Article  Google Scholar 

  • Jiang Z, Deng A D, Cai B H, 2011. Application of an adaptive generalized morphological filter based on the gradient method in rubbing acoustic emission signal denoise. Chinese Journal of Construction Machinery, 26(8): 87–92.

    Google Scholar 

  • Lei T F, Yang Y, 2011. Double operator morphological filters. Acta Automatica Sinica, 37(4): 449–463.

    Article  Google Scholar 

  • Li G F, 2009. Mode domain signal processing technique and its applicationin under water acoustic system. Doctor’s Thesis, Harbin Engineering University.

  • Li H, Lin Q Z, Wang Q J, Liu Q J, Wu Y Z, 2010. Research on spectrum denoising methods based on the combination of wavelet package transformation and mathematical morphology. Spectroscopy and Spectral Analysis, 30(3): 644–648.

    Google Scholar 

  • Maragos P, Schafer R, 1987a. Morphological filters-Part I: Their set-theoretic analysis and relations to linear shift-invariant filters. Acoustics, Speech and Signal Processing, 35(8): 1153–1169.

    Article  Google Scholar 

  • Maragos P, Schafer R, 1987b. Morphological filters-Part II: Their relations to median, order-statistic, and stack filters. Acoustics, Speech and Signal Processing, 35(8): 1170–1184.

    Article  Google Scholar 

  • Nie B S, He X Q, He J, Zhai S R, 2006, Research on removing noise of electromagnetic emission signals with wavelet transform. Journal of Taiyuan University of Technology, 37(5): 557–560.

    Google Scholar 

  • Qu L S, Zhang X N, Shen Y D, 2009. Theories and methods for machinery fault diagnosis. Xi’an: Xi’an Jiaotong University Press.

    Google Scholar 

  • Sa Z Y, Wang E Y, 2007. Short-time fractal fuzzy filtering of electromagnetic emission signal during the fracture of coal or rock. Chinese Journal of Radio Science, 22(2): 191–196.

    Google Scholar 

  • Shen L, 2010. Study on application of mornhologv in machinery fault diagnosis. Doctor’s Thesis, Zhejiang University.

  • Sun J P, 2011. Research on characteristics and key technology in coal mine internet of things. Journal of China Coal Society, 36(1): 167–171.

    Google Scholar 

  • Sun J P, Ren J B, Feng D W Zheng Z W, 2010. The measurement on the electromagnetic radiation property of electrical equipment at transformer station in the underground coalmine. Journal of China Coal Society, 35(5): 861–864.

    Google Scholar 

  • Sun J P, Wang F Z, 2009. Analysis of affection of electromagnetic interference on communication and monitoring and control system in coal mine underground. Industry and Mine Automation, (2): 23–27.

    Google Scholar 

  • Wang E Y, He X Q, Nie B S, Liu Z T, 2000. Principle of predicting coal and gas outburstusing electromagnetic emission. Journal of China University of Mining & Technology, 29(3): 225–229.

    Google Scholar 

  • Wang W, Zhao G J, Li Q, 2007. Study of novel adaptive denoising approach and its application. Computer Engineering and Applications, 43(26): 184–186.

    Google Scholar 

  • Zhang J C, Wu X J, 2007. Research on applications of morphological filtering in real-time signal processing. Chinese Journal of Sensors and Actuators, 20(4): 828–831.

    Google Scholar 

  • Zhang Q M, Liu H J, 2006. New power system sampled data processing method based on morphology theory. Electric Power Automation Equipment, 26(10): 45–49.

    Google Scholar 

  • Zhang W B, Zhou X J, Li J S, Shen L, Lin Y, Yang X Y, 2010. Purification of rotor center’s orbit based on generalized morphological filter. Journal of Zhejiang University (Engineering Science Edition), 44(5): 893–896.

    Google Scholar 

  • Zhao C H, 1998. Research on digital morphological filter theory and its algorithms. Doctor’s Thesis, Harbin Institute of Technology.

  • Zhao J, He Z Y, Qian Q Q, 2009. Detection of power quality disturbances utilizing generalized morphological filter and difference-entropy. Proceedings of the CSEE, 29(7): 121–127.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shi-hai Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, Sh., Wang, Ey. Removal of noises from electromagnetic radiation of coal or rock with EEMD-adaptive morphological filter. J Coal Sci Eng China 18, 330–336 (2012). https://doi.org/10.1007/s12404-012-0319-1

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12404-012-0319-1

Keywords

Navigation