Top

Arabian Journal for Science and Engineering

Published in:

05-01-2021 | Research Article-Civil Engineering

A Novel Denoising Model of Underwater Drilling and Blasting Vibration Signal Based on CEEMDAN

Authors: Yaxiong Peng, Yunsi Liu, Chao Zhang, Li Wu

Published in: Arabian Journal for Science and Engineering | Issue 5/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In underwater drilling and blasting engineering, the blasting vibration signal is mixed with a mass of noises due to the complexity of monitoring environment, the error of monitoring sensors and the reflection of propagation medium. In order to accurately obtain the characteristics of vibration signal, a novel denoising model is established. The complete ensemble empirical mode decomposition with adaptive noise is used to decompose the original signal, and the objective function of the filtering algorithm is used to obtain the optimal denoising signal. The results indicate that the model can not only successfully remove the high-frequency noise but also has no effect on the low-frequency signal components, which verifies the reliability and validity of the denoising model.

previous article Model Test of Interaction Between Load-Caused Landslide and Double-Row Anti-slide Piles by Transparent Soil Material

next article Artificial Intelligence Approach in Predicting the Effect of Elevated Temperature on the Mechanical Properties of PET Aggregate Mortars: An Experimental Study

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Monjezi, M.; Bahrami, A.; Yazdian, A.: Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int. J. Rock Mech. Min. 47(3), 476–480 (2009)CrossRef

Ghoshal, R.; Mitra, N.: Underwater explosion induced shock loading of structures: influence of water depth, salinity and temperature. Ocean Eng. 126(1), 22–28 (2016)CrossRef

Peng, Y.X.; Wu, L.; Chen, C.H.; Zhu, B.B.; Jia, Q.J.: Study on the robust regression of the prediction of vibration velocity in underwater drilling and blasting. Arab. J. Sci. Eng. 43, 5541–5549 (2018)CrossRef

Fei, H.L.; Liu, M.; Qu, G.J.; Gao, Y.: A method for blasting vibration signal denoising based on ensemble empirical mode decomposition-wavelet threshold. Explosion Shock Waves 38(1), 112–118 (2018)

Liang, M.D.; Wu, Z.; Xia, L.M.: Application of dual shift invariant wavelet transform in blasting vibration signal denoising: IEEE 3rd International Conference on Advanced Computing and Communication Systems. (2016). https://doi.org/10.1109/ICACCS.2016.7586357

Faria, M.L.L.D.; Cugnasca, C.E.; Amazonas, J.R.A.: Insights into IoT data and an innovative dwt-based technique to denoise sensor signals. IEEE Sensors J (2017). https://doi.org/10.1109/JSEN.2017.2767383CrossRef

Banjade, T.P.; Yu, S.; Ma, J.: Earthquake accelerogram denoising by wavelet-based variational mode decomposition. J. Seismol. 23, 649–663 (2019)CrossRef

Yang, D.; Ren, W.X.; Xiao, X.: Structure dynamic signal denoise using multi-scale emd. Adv. Mater. Res. 168–170, 611–2614 (2010)

Rakshit, M.; Das, S.: An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter. Biomed. Signal Process. Control 40, 140–148 (2018)CrossRef

10.

Wang, Z.L.; Chen, G.H.; Huang, Y.P.: Optimal white noise coefficient in EEMD corrected zero drift signal of blasting acceleration. Explosion Shock Waves 39(8), 084201 (2019)

11.

Zhang, L.; Yan, J.L.; Li, D.T.: Application of wavelet threshold denoising method in signal preprocessing for blast vibration: IEEE 3rd International Congress on Image and Signal Processing, pp. 4028–4031 (2010)

12.

Xie, Q.M.; Zhang, H.Z.; Gao, Y.; Cao, H.A.; Guo, S.Q.; Zhong, M.S.; Liu, H.Q.: Research on blasting vibration signal denoising based on lifting scheme. Appl. Mech. Mater. 713–715, 647–650 (2015)CrossRef

13.

Xia, L.M.; Zheng, W.; Liang, M.D.: Application of a comprehensive one dimensional wavelet threshold denoising algorithm in blasting signal. Inf. Technol. Intell. Transp. Syst. (2017). https://doi.org/10.1007/978-3-319-38771-0_51CrossRef

14.

Zhao, M.S.; Liang, K.S.; Luo, Y.F.; Xu, Y.P.: Application of EEMD in blasting vibration signal denoising. Blasting 28(2), 17–20 (2011)

15.

Yuan, H.P.; Liu, X.L.; Liu, Y.; Bian, H.B.; Chen, W.; Wang, Y.X.: Analysis of acoustic wave frequency spectrum characters of rock mass under blasting damage based on the HHT method. Adv. Civ. Eng. (2018). https://doi.org/10.1155/2018/9207476CrossRef

16.

Chen, Y.; Gan, D.; Liu, T.; Yuan, J.; Zhang, Y.; Jin, Z.: Random noise attenuation by a selective hybrid approach using empirical mode decomposition. J. Geophys. Eng. 12, 12–22 (2015)CrossRef

17.

Torres, M.E.; Colominas, M.A.; Gasto’n, S.: A complete ensemble empirical mode decomposition with adaptive noise: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. (2011). https://doi.org/10.1109/ICASSP.2011.5947265

18.

Colominas, M.A.; Schlotthauer, G.; Torres, M.E.: Improved complete ensemble EMD: a suitable tool for biomedical signal processing. Biomed. Signal Process. Control 14(1), 19–29 (2014)CrossRef

19.

Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.C.; Shih, H.H.; Zheng, Q.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceed. Royal Soc. Lond. 454(3), 903–995 (1998)MathSciNetCrossRef

20.

Li, Q.; Xu, W.L.; Zhang, D.; Li, N.; Feng, D.D.: An improved method to eliminate modal aliasing and false component in blast vibration signals. J. Vibrat. Shock 38(17), 197–204 (2019)

21.

Wu, Z.H.; Huang, N.E.: Ensemble empirical mode decomposition: a noise assisted data analysis method. Adv. Adapt. Data Anal. 1(1), 1–41 (2009)CrossRef

22.

Zheng, Y.; Yue, J.; Sun, X.F.; Chen, J.: Studies of filtering effect on internal solitary wave flow field data in the south china sea using EMD. Adv. Mater. Res. 518–523, 1422–1425 (2012)CrossRef

23.

Heidelberg, S.B.: Nyquist Sampling Theorem. Encyclopedia of Neuroscience (2011)

24.

Peng, Y.X.; Su, Y.; Wu, L.; Chen, C.H.: Study on the attenuation characteristics of seismic wave energy induced by underwater drilling and blasting. Shock Vib. (2019). https://doi.org/10.1155/2019/4367698CrossRef

25.

Si, Y.Q.; Guo, R.H.; Li, M.R.: Application of optimal noise reduction smooth model based on EMD and EEMD in seismic waves. CT Theory Appl. 29(1), 11–21 (2020)

26.

Li, T.; Wen, P.; Jayamaha, S.: Anaesthetic EEG signal denoise using improved nonlocal mean methods. Australas. Phys. Eng. Sci. Med. 37(2), 431–437 (2014)CrossRef

27.

Zhong, G.S.; Deng, Y.X.; Ao, L.P.: Study and application of translation invariant wavelet de-noising for blasting seismic signals: IEEE 2011 International Conference on Multimedia Technology. (2011). https://doi.org/10.1109/ICMT.2011.6002967

28.

Zhang, L.; Sun, X.J.; Zhan, Q.B.: Research of filtering method for blasting vibration signals based on CEEMD and correlation function property. Water Resour. Hydropower Eng. 48(6), 37–42 (2017)

29.

Zhang, X.L.; Jia, R.S.; Lu, X.M.; Peng, Y.J.; Zhao, W.D.: Identification of blasting vibration and coal-rock fracturing microseismic signals. Appl. Geophys. 15(2), 280–289 (2018)CrossRef

Title: A Novel Denoising Model of Underwater Drilling and Blasting Vibration Signal Based on CEEMDAN
Authors: Yaxiong Peng
Yunsi Liu
Chao Zhang
Li Wu
Publication date: 05-01-2021
Publisher: Springer Berlin Heidelberg
Published in: Arabian Journal for Science and Engineering / Issue 5/2021
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-020-05274-z

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 5/2021

Energy Dissipation in 3D Structure having Multiple Uplifts During Dynamic Loading

Mesoscopic Analysis of the Mechanical Properties of Beishan Granite Based on the Equivalent Crystal Model

High Pressure Conduit: A Good Alternative to the Air Compressors Used in the Flotation Process

Seismic Behavior and Damping Efficiency of Reinforced Rubberized Concrete Jacketing

Acoustic Emission Monitoring of RC Beams Corroded to Different Levels Under Flexural Loading

Plate Loading Tests on Clay with Construction and Demolition Materials

Premium Partners