Total views : 169

Advanced Filtering Methods Application for Sensitivity Enhancement during AE Testing of Operating Structures


  • National Research University Moscow Power Engineering Institute, Moscow, Russia


Background/Objectives: The article considers different filtering methods for acoustic emission data. The discussion of data filtering algorithms is aimed at improving the noise immunity of the acoustic emission system. Method: Noise filtering methods (frequency filtering, time series disorder detection, etc.) are examined for AE testing. Findings: One problem of the acoustic emission testing is a great number of noises affecting the diagnosis results. Electric noises, electromagnetic interference, background acoustic noise, rubbing noises are far from the full list of noises available during measurements. At the high level of noises, the operator has to increase the recording threshold of the acoustic emission impulses through reducing the testing sensitivity at the risk of missing a dangerous defect. Lack of the data filtering can result in an incorrect localization and erroneous definition of the danger level of acoustic emission source. Different noise types have been investigated and noise classification method according to the filtering complexity has been suggested to solve effectively the problem of the acoustic emission test data filtering. Wavelet-filtering efficiency for white stochastic noise removal has been shown. Algorithm for impulse noise filtering has been described. Improvements: The offered data processing approaches allow enhancing the sensitivity of AE testing especially for the operating structures.


Acoustic Emission (AE) of Operating Structures, Signal Processing, Time Series Analysis.

Full Text:

 |  (PDF views: 165)


  • Barat V, Borodin Y, Kuzmin A. Intelligent AE signal filtering methods. Journal of Acoustic Emission. 2010; 28:109–19.
  • Mallat S. A wavelet tour of signal processing: the sparse way, 3rd ed., Academic Press; 2009.
  • Donoho DL. De-noising by soft-thresholding. IEEE Transaction on Information Theory. 1995; 41(3):613–27. DOI: 10.1109/18.382009.
  • Barat V, Elizarov S, Bolokhova I, Bolokhov E. Application of ICI Principle for AE Data Processing. Journal of Acoustic Emission. 2011; 29:124–36.
  • Barat V, Chernov D, Elizarov S. Discovering data flow discords for enhancing noise immunity of acoustic-emission testing. Russian Journal of Nondestructive Testing. 2016; 52(6):347–56. DOI: 10.1134/S1061830916060024.
  • Builo SI. On the information capacity of the invariant method in the analysis of resampled acoustic emission streams. Russian Journal of Nondestructine Testing. 2009; 45(11):775–8. DOI: 10.1134/S1061830909110035.
  • Schelter B, Winterhalder M, Timmern J. Handbook of time series analysis: Recent theoretical developments and applications. Wiley; 2006.
  • Kramer N Sh. Theory of probability and mathematical statistics. Moscow: UNITI-DANA; 2010. [in Russian]


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.