Skip to main content
SpringerLink
Log in

Soft Filtering of Acoustic Emission Signals Based on the Complex Use of Huang Transform and Wavelet Analysis

  • Conference paper
  • First Online:
Lecture Notes in Computational Intelligence and Decision Making (ISDMCI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1020))

Abstract

The paper presents the results of the research concerning development of acoustic emission signals soft filtering model based on the complex use of Huang transform and wavelet analysis. The acoustic emission signals which were generated during crack progression from initiation to final failure with several distinct phases have been used as the experimental signals during the simulation process. The families of biorthogonal wavelets were used during the filtering process. The Shannon entropy criterion which was calculated with the use of James-Stein estimator was used as the main criterion to estimate the filtering process quality. The optimal parameters of the wavelet filter (type of wavelet, level of wavelet decomposition, value of the thresholding coefficient) were determined based on the minimum value of the Shannon entropies ratio which were calculated for filtered signal and for allocated noise component.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gong K, Hu J (2019) Online detection and evaluation of tank bottom corrosion based on acoustic emission. In: Springer series in geomechanics and geoengineering, (216039), pp 1284–1291. https://doi.org/10.1007/978-981-10-7560-5_118

    Google Scholar 

  2. Ridgley KE, Abouhussien AA, Hassan AAA, Colbourne B (2019) Characterisation of damage due to abrasion in SCC by acoustic emission analysis. Mag Concr Res 71(2):85–94. https://doi.org/10.1680/jmacr.17.00445

    Article  Google Scholar 

  3. Berte R, Della Picca F, Poblet M, Li Y, Cortés E, Craster RV, Maier SA, Bragas AV (2018) Acoustic far-field hypersonic surface wave detection with single plasmonic nanoantennas. Phys Rev Lett 121(25). https://doi.org/10.1103/PhysRevLett.121.253902. Article no 253902

  4. Zhang X, Zou Z, Wang K, Hao Q, Wang Y, Shen Y, Hu H (2018) A new rail crack detection method using LSTM network for actual application based on AE technology. Appl Acoust 142:78–86. https://doi.org/10.1016/j.apacoust.2018.08.020

    Article  Google Scholar 

  5. Xu J, Shu S, Han Q, Liu C (2018) Experimental research on bond behavior of reinforced recycled aggregate concrete based on the acoustic emission technique. Constr Build Mater 191:1230–1241. https://doi.org/10.1016/j.conbuildmat.2018.10.054

    Article  Google Scholar 

  6. Su F, Li T, Pan X, Miao M (2018) Acoustic emission responses of three typical metals during plastic and creep deformations. Exp Tech 42(6):685–691. https://doi.org/10.1007/s40799-018-0274-x

    Article  Google Scholar 

  7. Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng 82(1):35–45

    Article  Google Scholar 

  8. Wiener N (1949) Extrapolation, interpolation, and smoothing of stationary time series. Wiley, New York

    MATH  Google Scholar 

  9. Izonin I, Trostianchyn A, Duriagina Z, Tkachenko R, Tepla T, Lotoshynska N (2018) The combined use of the wiener polynomial and SVM for material classification task in medical implants production. Int J Intell Syst Appl 10(9):40–47. https://doi.org/10.5815/ijisa.2018.09.05

    Article  Google Scholar 

  10. Staub S, Andrä H, Kabel M (2018) Fast FFT based solver for rate-dependent deformations of composites and nonwovens. Int J Solids Struct 154:33–42. https://doi.org/10.1016/j.ijsolstr.2016.12.014

    Article  Google Scholar 

  11. Cui L, Ma F, Gu Q, Cai T (2018) Time-frequency analysis of pressure pulsation signal in the chamber of self-resonating jet nozzle. Int J Pattern Recogn Artif Intell 32(11). https://doi.org/10.1142/S0218001418580065. Article no 1858006

    Article  Google Scholar 

  12. Zalik RA (2019) On orthonormal wavelet bases. J Comput Anal Appl 27(5):790–797

    Google Scholar 

  13. Riabova S (2018) Application of wavelet analysis to the analysis of geomagnetic field variations. J Phys Conf Ser 1141(1). https://doi.org/10.1088/1742-6596/1141/1/012146. Article no 012146

    Google Scholar 

  14. Bodyanskiy Y, Perova I, Vynokurova O, Izonin I (2018) Adaptive wavelet diagnostic neuro-fuzzy network for biomedical tasks. In: 14th international conference on advanced trends in radioelectronics, telecommunications and computer engineering, TCSET 2018 - proceedings, April 2018, pp 711–715. https://doi.org/10.1109/TCSET.2018.8336299

  15. Babichev S, Škvor J, Fišer J, Lytvynenko V (2018) Technology of gene expression profiles filtering based on wavelet analysis. Int J Intell Syst Appl 10(4):1–7. https://doi.org/10.5815/ijisa.2018.04.01

    Article  Google Scholar 

  16. Huang N, Shen Z, Long S, Wu M, Shih H, Zheng N, Yen N, Tung C, Liu H (1998) The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc Math Phys Eng Sci 454:903–995

    Article  MathSciNet  Google Scholar 

  17. Huang NE, Wu Z (2008) A review on Hilbert-Huang transform: method and its applications to geophysical studies. Rev Geophys 46(2). https://doi.org/10.1029/2007RG000228. Article no RG2006

  18. Li W, Kuang G, Xiong B (2018) Decomposition of multicomponent micro-Doppler signals based on HHT-AMD. Appl Sci (Switz) 8(10). https://doi.org/10.3390/app8101801. Article no 1801

    Article  Google Scholar 

  19. Soualhi A, Medjaher K, Zerhouni N (2015) Bearing health monitoring based on Hilbert-Huang transform, support vector machine, and regression. IEEE Trans Instrum Meas 64(1):52–62. https://doi.org/10.1109/TIM.2014.2330494. Article no 6847199

    Article  Google Scholar 

  20. Susanto A, Liu C-H, Yamada K, Hwang Y-R, Tanaka R, Sekiya K (2018) Application of Hilbert-Huang transform for vibration signal analysis in end-milling. Precis Eng 53:263–277. https://doi.org/10.1016/j.precisioneng.2018.04.008

    Article  Google Scholar 

  21. Susanto A, Liu C-H, Yamada K, Hwang Y-R, Tanaka R, Sekiya K (2018) Milling process monitoring based on vibration analysis using Hilbert-Huang transform. Int J Autom Tech 12(5):688–698. https://doi.org/10.20965/ijat.2018.p0688

    Article  Google Scholar 

  22. Trusiak M, Styk A, Patorski K (2018) Hilbert-Huang transform based advanced Bessel fringe generation and demodulation for full-field vibration studies of specular reflection micro-objects. Opt Lasers Eng 110:100–112. https://doi.org/10.1016/j.optlaseng.2018.05.021

    Article  Google Scholar 

  23. Oweis RJ, Abdulhay EW (2011) Seizure classification in EEG signals utilizing Hilbert-Huang transform. BioMed Eng Online, 10. https://doi.org/10.1186/1475-925X-10-38. Article no 38

    Article  Google Scholar 

  24. Huang NE, Wu M-L, Qu W, Long SR, Shen SSP (2003) Applications of Hilbert-Huang transform to non-stationary financial time series analysis. Appl Stoch Models Bus Ind 19(3):245–268. https://doi.org/10.1002/asmb.501

    Article  MathSciNet  MATH  Google Scholar 

  25. Yuan H, Liu X, Liu Y, Bian H, Chen W, Wang Y (2018) 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/10.1155/2018/9207476. Article no 9207476

    Google Scholar 

  26. Babichev S, Lytvynenko V, Osypenko V (2017) Implementation of the objective clustering inductive technology based on DBSCAN clustering algorithm. In: Proceedings of the 12th international scientific and technical conference on computer sciences and information technologies, CSIT 2017, vol 1, pp 479–484. https://doi.org/10.1109/STC-CSIT.2017.8098832. Article no 8098832

  27. Babichev S, Lytvynenko V, Gozhyj A, Korobchynskyi M, Voronenko M (2019) A fuzzy model for gene expression profiles reducing based on the complex use of statistical criteria and Shannon entropy. Adv Intell Syst Comput 754:545–554. https://doi.org/10.1007/978-3-319-91008-6_55

    Article  Google Scholar 

  28. Bidyuk P, Gozhyj A, Kalinina I, Gozhyj V (2017) Methods for processing uncertainties in in solving dynamic planning problems. In: Proceedings of the 12th international scientific and technical conference on computer sciences and information technologies, CSIT 2017, vol 1, pp 151–155. https://doi.org/10.1109/STC-CSIT.2017.8098757. Article no 8098757

  29. Bidyuk P, Gozhyj A, Kalinina I, Gozhyj V (2018) Analysis of uncertainty types for model building and forecasting dynamic processes. Adv Intell Syst Comput 689:66–78. https://doi.org/10.1007/978-3-319-70581-1_5

    Article  Google Scholar 

  30. Hausser J, Strimmer K (2009) Entropy inference and the james-stein estimator, with application to nonlinear gene association networks. J Mach Learn Res 10:1469–1484

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Jan Evangelista Purkyně University in Ústí nad Labem, Ústí nad Labem, Czech Republic

    Sergii Babichev

  2. Ukrainian Academy of Printing, Lviv, Ukraine

    Sergii Babichev

  3. Kherson State Maritime Academy, Kherson, Ukraine

    Oleksandr Sharko

  4. Kherson National Technical University, Kherson, Ukraine

    Artem Sharko

  5. National Metallurgical Academy of Ukraine, Dnipro, Ukraine

    Oleksandr Mikhalyov

Authors
  1. Sergii Babichev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oleksandr Sharko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Artem Sharko
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Oleksandr Mikhalyov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sergii Babichev .

Editor information

Editors and Affiliations

  1. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

  2. Jan Evangelista Purkyně University in Ústi and Labem, Usti and Labem, Czech Republic

    Sergii Babichev

  3. Lublin University of Technology, Lublin, Poland

    Waldemar Wójcik

  4. IT Step University, Lviv, Ukraine

    Olena Vynokurova

  5. Kherson National Technical University, Kherson, Ukraine

    Svetlana Vyshemyrskaya

  6. Kherson National Technical University, Kherson, Ukraine

    Svetlana Radetskaya

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Babichev, S., Sharko, O., Sharko, A., Mikhalyov, O. (2020). Soft Filtering of Acoustic Emission Signals Based on the Complex Use of Huang Transform and Wavelet Analysis. In: Lytvynenko, V., Babichev, S., Wójcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2019. Advances in Intelligent Systems and Computing, vol 1020. Springer, Cham. https://doi.org/10.1007/978-3-030-26474-1_1

Download citation

Publish with us

Policies and ethics

Search

Navigation