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Analysis of Acoustic Emission Signal for Crack Detection and Distance Measurement on Steel Structure

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Abstract

Acoustic emission (AE) technique has been merged to a promising method for structural health monitoring in non-destructive technique. So an analysis of the AE signal is becoming a very important research component. In this paper, an algorithm is developed for detection of the crack signal among different noise signals since the AE signal is also generated by several means like any impact or rubbing action on the structure which may give erroneous results. An AE monitoring system is developed with three experimental setups to generate three types of AE signals from three dissimilar sources. Thus, an algorithm is developed to identify the crack signal by comparing the parameters of different signals acquired from different sources using some signal processing techniques such as parameter based analysis, waveform based analysis e.g. fast Fourier transform, continuous wavelet transform, cross-correlation coefficient, magnitude coherence coefficient, and energy distribution. After identification of the crack signal, the distance of the crack source has been calculated by analysing the signal in time–frequency domain also an algorithm has been designed to calculate the velocity of the acoustic wave more accurately and consequently the distance of the crack.

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References

  1. Nor, N.M.: Structural Health Monitoring Through Acoustic Emission. Woodhead Publishing Series in Civil and Structural Engineering, pp. 123–146. Woodhead Publishing, Sawston (2018)

    Google Scholar 

  2. Crivelli, D., Bland, S.: Structural health monitoring via acoustic emission. Reinforced Plast. 60(6), 390–392 (2016)

    Article  Google Scholar 

  3. Keshtgar, A., Modarres, M.: Detecting crack initiation based on acoustic emission. Chem. Eng. Trans. 33, 547–552 (2013)

    Google Scholar 

  4. Massy, D., Mazen, F., Landru, D., Mohamed, N.B., Tardif, S., et al.: Crack front interaction with self-emitted acoustic waves. Phys. Rev. Lett. 121(19), 195501 (2018)

    Article  Google Scholar 

  5. Teng, X., Zhang, X., Fan, Y., Zhang, D.: Evaluation of cracks in metallic material using a self-organized data-driven model of acoustic echo-signal. Appl. Sci. 9, 95 (2019). https://doi.org/10.3390/app9010095

    Article  Google Scholar 

  6. Steen, C.V., Pahlavan, L., Wevers, M., Verstryngea, E.: Localisation and characterisation of corrosion damage in reinforced concrete by means of acoustic emission and X-ray computed tomography. Constr. Build. Mater. 197(10), 21–29 (2019)

    Article  Google Scholar 

  7. Zhang, X., Wang, K., Wang, Y., Shen, Y., Hu, H.: Rail crack detection using acoustic emission technique by joint optimization noise clustering and time window feature detection. Appl. Acoust. 160, 107141 (2020)

    Article  Google Scholar 

  8. Karimian, S.F., Modarres, M., Bruck, H.A.: A new method for detecting fatigue crack initiation in aluminum alloy using acoustic emission waveform information entropy. Eng. Fract. Mech. 223, 106771 (2020)

    Article  Google Scholar 

  9. Prajna, K., Mukhopadhyay, C.K.: Fractional fourier transform based adaptive filtering techniques for acoustic emission signal enhancement. J. Nondestruct. Eval. 39(1), 14 (2020)

    Article  Google Scholar 

  10. Ghimire, R., Anderson, G., Delfanian, F.: Acoustic Emission test on steel/composite and steel/composite/steel built-up sections. In: Proceedings of the ASME 2011 International Mechanical Engineering Congress & Exposition

  11. Gholizadeh, S., Leman, Z., Baharudin, B.T.: A review of acoustic emission technique in engineering. Struct. Eng. Mech. 54(6), 1075–1095 (2015)

    Article  Google Scholar 

  12. Yılmazer, P., Amini, A., Papaelias, M.: The Structural health condition monitoring of rail steel using acoustic emission techniques. In: 51st Annual conference of the British Institute of Non-Destructive Testing 2012

  13. Kaewunrue, S., et al.: State-of-the-art review of railway track resilience monitoring. MDPI Infrastruct. 3(1), 3 (2018)

    Article  Google Scholar 

  14. Ono, K.: Structural health monitoring of large structures using acoustic emission—case histories. Appl. Sci. 9(21), 4602 (2019)

    Article  Google Scholar 

  15. Khan, M.T.I.: Structural Health Monitoring by Acoustic Emission Technique, No. 5752, vol. 10. IntechOpen, Rijeka (2018)

    Google Scholar 

  16. Clark, A., Kaewunruen, S., Janeliukstis, R., Papaelias, M.: Damage detection in railway prestressed concrete sleepers using acoustic emission. Conf. Ser.: Mater. Sci. Eng. 251, 012068 (2017)

    Google Scholar 

  17. Janeliukstis, R., Kaewunruen, S.: A novel separation technique of flexural loading-induced acoustic emission sources in railway prestressed concrete sleepers. IEEE Access 7, 51426–51440 (2019)

    Article  Google Scholar 

  18. Janeliukstisa, R., Clarkb, A., Papaeliasc, M., Kaewunrue, S.: Flexural cracking-induced acoustic emission peak frequency shift in railway prestressed concrete sleeper. Eng. Struct. 178, 493–505 (2019)

    Article  Google Scholar 

  19. Janeliukstisa, R., Ručevskisa, S., Kaewunrue, S.: Mode shape curvature squares method for crack detection in railway prestressed concrete sleepers. Eng. Fail. Anal. 105, 386–401 (2019)

    Article  Google Scholar 

  20. Shi, S., Kaewunruen, S., Papaelias, M., et al.: Quantitative monitoring of brittle fatigue crack growth in railway steel using acoustic emission. J. Rail Rapid Transit 232, 1211–1224 (2017)

    Article  Google Scholar 

  21. Swit, G.: Acoustic emission method for locating and identifying active destructive processes in operating facilities. MPDI Appl. Sci. 8(8), 1295 (2018)

    Article  Google Scholar 

  22. Krampikowska, A., Swit, G., et al.: The use of the acoustic emission method to identify crack growth in 40CrMo steel. MDPI Mater. 12(13), 2140 (2019)

    Google Scholar 

  23. Angulo, Á., Tang, J., Khadimallah, A., Soua, S., Mares, C., Gan, T.-H.: Acoustic emission monitoring of fatigue crack growth in mooring chains. MPDI Appl. Sci. 9(11), 2187 (2019)

    Article  Google Scholar 

  24. Kaphle, M.: Analysis of acoustic emission data for accurate damage assessment for structural health monitoring application. Dissertation (2012)

  25. Meng, X., Liu, W., Ding, E.: The research of acoustic emission signal classification. In: 2011 Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Dalian, pp. 41–44 (2011)

  26. Morizet, N., Godin, N., Tang, J., Maillet, E., Fregonese, M., Normand, B.: Classification of acoustic emission signals using wavelets and random forests: application to localized corrosion. Mech. Syst. Signal Process. 70–71, 1026–1037 (2016)

    Article  Google Scholar 

  27. Facciotto, N., Martinez, M.J., Troiani, E.: Source identification and classification of acoustic emission signals by a SHAZAM inspired pattern recognition algorithm. In: Conference: International Workshop on Structural Health Monitoring (IWSHM 2017) At: Palo Alto California, USA, September 2017

  28. Allen, J.B.: Short term spectral analysis, synthesis, and modification by discrete Fourier transform. IEEE Trans. Acoust. Speech Signal Process. ASSP-25ASSP-25, 235–238 (1977)

    Article  Google Scholar 

  29. Allen, J.B.: Application of the short-time Fourier transform to speech processing and spectral analysis. In: Proceedings on IEEE ICASSP-82, pp. 1012–1015 (1982)

  30. Eaton, M., Pullin, R., Holford, K., Evans, S., Featherston, C., Rose, A.: Use of macro fibre composite transducers as acoustic emission sensors. Remote Sens. 1(2), 68–79 (2009)

    Article  Google Scholar 

  31. Kurz, J.H., Finck, F., Grosse, C.U., Reinhardt, H.W.: Similarity matrices as a new feature for acoustic emission analysis of concrete. In: 26th European Conference on Acoustic Emission Testing; 2004 September 15-17; Berlin: German Society for Non Destructive Testing (DGZfP) (2004)

  32. Mathworks: MATLAB Users Guide. Mathworks, Natick, MA (2009)

    Google Scholar 

  33. Grosse, C.U., Finck, F., Kurz, J.H., Reinhardt, H.W.: Improvements of AE technique using wavelet algorithms, coherence functions and automatic data analysis. Constr. Build. Mater. 18(3), 203–213 (2004)

    Article  Google Scholar 

  34. Hsu, N.N., Breckenridge, F.R.: Characterization and calibration of acoustic emission sensors. Mater. Eval. 39, 60–68 (1981)

    Google Scholar 

  35. Jingpin, J., Bin, W., Cunfu, H.: Acoustic emission source location methods using mode and frequency analysis. Struct. Control Health Monit. 15, 642–651 (2008)

    Article  Google Scholar 

  36. Proakis, J.G., Manolakis, D.G.: Digital signal processing: principles algorithms and applications, 4th edn. Pearson Prentice Hall, Upper Saddle River, NJ (2007)

    Google Scholar 

  37. Lynn, P.A.: An Introduction to the Analysis and Processing of Signals. Macmillan Press, London (1973)

    Google Scholar 

  38. http://www.me.sc.edu/research/lamss/html/software.html

  39. Liu, C.L.: A tutorial of the wavelet transform (2010)

  40. Addison, P.S.: Wavelet transforms and the ECG: a review. In: IOP Science, 8 Aug. 2005

  41. Mallat, S.: A Wavelet Tour of Signal Processing, 2nd edn. Academic Press, Cambridge (1999)

    MATH  Google Scholar 

  42. Ziola, S.M., Gorman, M.R.: Source location in thin plates using cross-correlation. J. Acoust. Soc. Am. 90, 2551 (1991)

    Article  Google Scholar 

  43. Mukherjee, A., Maurya, A., Karmakar, P., Bhattacharjee, P.: Analysis of signal for determination of crack distance from acoustic emission sensor of a steel bridge. In: 2019 International Conference on Computing, Power and Communication Technologies (GUCON), NCR New Delhi, India, pp. 969–974 (2019)

  44. Mukherjee, A., Maurya, A.: Identification of different acoustic emission signal sources. In: 2018 International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC) (2018)

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Acknowledgement

This research work has been supported by “Department of Science and Technology”, Government of India.

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Correspondence to Arpita Mukherjee.

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Mukherjee, A., Banerjee, A. Analysis of Acoustic Emission Signal for Crack Detection and Distance Measurement on Steel Structure. Acoust Aust 49, 133–149 (2021). https://doi.org/10.1007/s40857-020-00208-z

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