A Quantitative Review of Air-Coupled Ultrasonic Lamb Wave Analysis Based on Signal Transformations
Source abstract
Abstract
Abstract Lamb wave detection is increasingly being utilized in the industry due to its extensive coverage area, high signal detection efficiency, and ease of operation. This paper offers a quantitative review of eight signal transformation methods utilized for de-noising and time-frequency analysis of Lamb waves, which include Fourier transform (FT), singular value decomposition (SVD), short-time Fourier transform (STFT), Wigner–Ville distribution (WVD), wavelet transform (WT), S-transform, Hilbert–Huang transform (HHT), as well as empirical mode decomposition (EMD) and its improved algorithms. The performances of signal transformations on denoising and defect location are assessed quantitatively using the signal-to-noise ratio (SNR) and time-of-flight (ToF). The results demonstrate that the complete ensemble EMD with adaptive noise (CEEMDAN) is able to suppress noise effectively while maintaining the primary features of the signal in an adaptive manner. Additionally, the continuous WT can obtain a more accurate time-frequency distribution, thereby providing the superior analytical ability for dispersive lamb wave signals with respect to positioning on the time axis.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Acoustic Wave Resonator Technologies · Underwater Acoustics Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (60)
Show source-provided references
1. Chen, X. and Wu, H.K., Ultrasonic Lamb wave measurement and data analysis system, Adv. Mater. Res., 2012, vols. 457–458, pp. 701–704.10.4028/www.scientific.net/AMR.457-458.701 ↗
2. Castaings, M., Le Clezio, E., and Hosten, B., Modal decomposition method for modeling the interaction of Lamb waves with cracks, J. Acoust. Soc. Am., 2002, vol. 112, no. 6, pp. 2567–2582.10.1121/1.1500756 ↗
3. Vasil’ev, A.V., Biryukov, D.Y., and Zatsepin, A.F., Ultrasonic testing of butt joints in electric steel plates using Lamb waves, Russ. J. Nondestr. Test., 2023, vol. 59, no. 1, pp. 11–21.10.1134/S1061830923700171 ↗
4. Luo, K., Chen, L., and Liang, W., Numerical simulation of carbon fiber reinforced polymer composite delamination damage identification using Lamb wave and filtered back-projection method, Russ. J. Nondestr. Test., 2022, vol. 58, no. 10, pp. 917–925.10.1134/S1061830922700024 ↗
5. Deng, M., Characterization of surface properties of a solid plate using nonlinear Lamb wave approach, Ultrasonics, 2006, vol. 44, pp. 1157–1162.10.1016/j.ultras.2006.05.158 ↗
6. Goldstein, R.V. and Kuznetsov, S.V., Long-wave asymptotics of Lamb waves, Mech. Solids, 2018, vol. 52, no. 6, pp. 700–707.10.3103/S0025654417060097 ↗
7. Burkov, M.V., Eremin, A.V., Byakov, A.V., et al., Impact damage detection in laminate and honeycomb CFRPs using Lamb wave ultrasonic sensing, Russ. J. Nondestr. Test., 2021, vol. 57, no. 2, pp. 114–124.10.1134/S1061830921020042 ↗
8. Ze-Yu, D., Wang, H., Yang, X., et al., Research for evaluation method based on Lamb waves for thickness of ship deck beams, Russ. J. Nondestr. Test., 2020, vol. 56, no. 7, pp. 556–565.10.1134/S1061830920070049 ↗
9. Sharma, A., Kumar, S., and Tyagi, A., Noise filtering techniques for Lamb waves in structural health monitoring, Multidiscip. Model. Mater. Struct., 2018, vol. 14, no. 4, pp. 676–694.10.1108/MMMS-08-2017-0089 ↗
10. Chen, X. and Li, J., Noise reduction for ultrasonic Lamb wave signals by empirical mode decomposition and wavelet transform, J. Vibroeng., 2013, vol. 15, no. 3, pp. 1157–1165.
11. Siqueira, M.H.S., Gatts, C.E.N., Da Silva, R.R., et al., The use of ultrasonic guided waves and wavelets analysis in pipe inspection, Ultrasonics, 2004, vol. 41, no. 10, pp. 785–797.10.1016/j.ultras.2004.02.013 ↗
12. Tian, Z. and Yu, L., Lamb wave frequency–wavenumber analysis and decomposition, J. Intel. Mater. Syst. Struct., 2014, vol. 25, no. 9, pp. 1107–1123.10.1177/1045389X14521875 ↗
13. Rizzo, P. and Lanza Di Scalea, F., Discrete wavelet transform to improve guided-wave-based health monitoring of tendons and cables, Smart Struct. Mater. 2004 (San Diego, 2004).10.1117/12.539847 ↗
14. Wang, Z., Qiao, P., and Shi, B., Application of soft-thresholding on the decomposed Lamb wave signals for damage detection of plate-like structures, Measurement, 2016, vol. 88, pp. 417–427.10.1016/j.measurement.2015.10.001 ↗
15. Perov, D.V., Rinkevich, A.B., and Smorodinskii, Y.G., Wavelet filtering of signals from ultrasonic flaw detector, Russ. J. Nondestr. Test., 2002, vol. 38, pp. 869–882.10.1023/A:1023852822195 ↗
16. Rinkevich, A.B. and Perov, D.V., A wavelet analysis of acoustic fields and signals in ultrasonic nondestructive testing, Russ. J. Nondestr. Test., 2005, vol. 41, no. 2, pp. 93–101.10.1007/s11181-005-0134-5 ↗
17. Xu, K., Minonzio, J., Ta, D., et al., Sparse SVD method for high-resolution extraction of the dispersion curves of ultrasonic guided waves, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 2016, vol. 63, no. 10, pp. 1514–1524.10.1109/TUFFC.2016.2592688 ↗
18. Wang, P., Zhou, W., and Li, H., A singular value decomposition-based guided wave array signal processing approach for weak signals with low signal-to-noise ratios, Mech. Syst. Signal Process., 2020, vol. 141, p. 106450.10.1016/j.ymssp.2019.106450 ↗
19. Le, M., Kim, J., Kim, S., et al., B-scan ultrasonic testing of rivets in multilayer structures based on shorttime Fourier transform analysis, Measurement, 2018, vol. 128, pp. 495–503.10.1016/j.measurement.2018.06.049 ↗
20. Sharma, G.K., Kumar, A., Rao, B.P., et al., Continuous wavelet transform analysis for the enhancement of signal-to-noise ratio in coarse grain austenitic stainless steel, Mater. Eval., 2014, vol. 72, no. 12.
21. Wandowski, T., Malinowski, P., and Ostachowicz, W., Damage localization using various signal filtering approaches, Int. Conf. Noise Vib. Eng. USD2010 (Leuven, 2010), pp. 1135–1149.
22. Zhu, Y., Xu, C., and Xiao, D., Denoising ultrasonic echo signals with generalized S transform and singular value decomposition, Trait. Signal, 2019, vol. 36, no. 2.10.18280/ts.360203 ↗
23. Dai, D. and He, Q., Structure damage localization with ultrasonic guided waves based on a time-frequency method, Signal Process., 2014, vol. 96, pp. 21–28.10.1016/j.sigpro.2013.05.025 ↗
24. Mao, Y. and Que, P., Noise suppression and flaw detection of ultrasonic signals via empirical mode decomposition, Russ. J. Nondestr. Test., 2007, vol. 43, no. 3, pp. 196–203.10.1134/S1061830907030096 ↗
25. Sun, W., Peng, Y., and Xu, J., A denoising method for laser ultrasonic signal based on EMD, J. Sandong Univ., 2008, vol. 38, no. 5, pp. 1–6.
26. Sharma, G.K., Kumar, A., Jayakumar, T., et al., Ensemble empirical mode decomposition based methodology for ultrasonic testing of coarse grain austenitic stainless steels, Ultrasonics, 2015, vol. 57, pp. 167–178.10.1016/j.ultras.2014.11.008 ↗
27. Gong, W., Liu, Q., Du, W., et al., A novel denoising algorithm of electromagnetic ultrasonic detection signal based on improved EEMD method, J. Electr. Comput. Eng., 2018, vol. 2018, pp. 1–13.10.1155/2018/5405639 ↗
28. Yeh, J., Shieh, J., and Huang, N.E., Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method, Adv. Adapt. Data Anal., 2010, vol. 2, no. 2, pp. 135–156.10.1142/S1793536910000422 ↗
29. Yu, J.M. and Zhang, Z., Research on feature extraction for ultrasonic echo signal based on EEMD approach, Trans. Tech. Publ., 2013.10.4028/www.scientific.net/AMM.321-324.1311 ↗
30. Wang, B.X., Yang, H.T., Wang, Q., et al., Bridge vibration signal optimization filtering method based on improved CEEMD-multis-cale permutation entropy analysis, J. Jilin Univ. (Eng. Technol. Ed.), 2020, vol. 50, no. 1, pp. 216–226.
31. Yang, Y., Li, S., Li, C., et al., Research on ultrasonic signal processing algorithm based on CEEMDAN joint wavelet packet thresholding, Measurement, 2022, vol. 201, p. 111751.10.1016/j.measurement.2022.111751 ↗
32. Dai, D. and He, Q., Structure damage localization with ultrasonic guided waves based on a time-frequency method, Signal Process., 2014, vol. 96, pp. 21–28.10.1016/j.sigpro.2013.05.025 ↗
33. Xiao, W.F. and Lu, C., Measuring the disperse curves of air-coupled lamb waves in glass fibre reinforced aluminium laminate based on time-frequency analysis, Mater. Res. Innov., 2015, vol. 19 (sup9), pp. S9–S106.10.1179/1432891715Z.0000000001934 ↗
34. Niethammer, M., Jacobs, L.J., Qu, J., et al., Time-frequency representation of Lamb waves using the reassigned spectrogram, J. Acoust. Soc. Am., 2000, vol. 107, no. 5, pp. L19–L24.10.1121/1.428894 ↗
35. Xu, H., Xu, C., Zhou, S., et al., Time-frequency analysis for nonlinear Lamb wave signal, 2009 2nd Int. Congr. Imag. Signal Proces. (Tianjin, 2009).10.1109/CISP.2009.5303708 ↗
36. Dong, J. and Yin, M., Analysis of guided wave signal based on LabVIEW and STFT, 2010 Int. Conf. Comput. Mechatron. Control Electron. Eng. (Changchun, 2010).
37. Lu, Z., Ma, F., Yang, C., et al., A novel method for Estimating Time of Flight of ultrasonic echoes through short-time Fourier transforms, Ultrasonics, 2020, vol. 103, p. 106104.10.1016/j.ultras.2020.106104 ↗
38. Duarte, R.M., Villanueva, J.M.M., Costa, M.M., et al., Ultrasonic time of flight estimation for wind speed measurement based on time-frequency domain using STFT, 19th IMEKO TC-4 Symp. (Barcelona, 2013).
39. Berriman, J.R., Hutchins, D.A., Neild, A., et al., The application of time-frequency analysis to the aircoupled ultrasonic testing of concrete, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 2006, vol. 53, no. 4, pp. 768–776.10.1109/TUFFC.2006.1621504 ↗
40. Rizvi, S.H.M. and Abbas, M., An advanced Wigner-Ville time-frequency analysis of Lamb wave signals based upon an autoregressive model for efficient damage inspection, Meas. Sci. Technol., 2021, vol. 32, no. 9, p. 95601.10.1088/1361-6501/abef3c ↗
41. Giurgiutiu, V. and Yu, L., Comparison of short-time Fourier transform and wavelet transform of transient and tone burst wave propagation signals for structural health monitoring, 4th Int. Workshop Struct. Health Monit. (Standford, 2003), pp. 1267–1274.
42. Liu, T., Li, J., Cai, X., et al., A time-frequency analysis algorithm for ultrasonic waves generating from a debonding defect by using empirical wavelet transform, Appl. Acoust., 2018, vol. 131, pp. 16–27.10.1016/j.apacoust.2017.10.002 ↗
43. Xiao, W.F. and Lu, C., Measuring the disperse curves of air-coupled lamb waves in glass fibre reinforced aluminium laminate based on time-frequency analysis, Mater. Res. Innov., 2015, vol. 19, suppl. 9, pp. S9–S106.10.1179/1432891715Z.0000000001934 ↗
44. Da Silva, J.H.B., de Oliveira, L.D., and Villanueva, J.M.M., Application of wavelet transform for ultrasonic time of flight estimation, 2022 6th Int. Symp. Instrum. Syst. Circuits Transducers (INSCIT) (Porto Alegre, 2022).10.1109/INSCIT55544.2022.9913752 ↗
45. Benammar, A., Drai, R., and Guessoum, A., Ultrasonic flaw detection using threshold modified S-transform, Ultrasonics, 2014, vol. 54, no. 2, pp. 676–683.10.1016/j.ultras.2013.09.004 ↗
46. Abdessalem, B., Redouane, D., Ahmed, K., et al., Enhancement of phased array ultrasonic signal in composite materials using TMST algorithm, Phys. Procedia, 2015, vol. 70, pp. 488–491.10.1016/j.phpro.2015.08.292 ↗
47. Xu, J. and Wei, H., Ultrasonic testing analysis of concrete structure based on S transform, Shock Vib., 2019, vol. 2019, pp. 1–9.
48. Quek, S., Tua, P., and Wang, Q., Comparison of Hilbert–Huang, wavelet, and Fourier transforms for selected applications, in The Hilbert–Huang Transform in Engineering, Boca Raton: CRC Press, 2005, pp. 213–244.
49. Zhang, H., Qi, X., Sun, X., et al., Application of Hilbert–Huang transform to extract arrival time of ultrasonic lamb waves, 2008 Int. Conf. Audio Language Imag. Proces. (Shanghai, 2008).
50. Zhang, Y., Wang, S., Huang, S., et al., Mode recognition of Lamb wave detecting signals in metal plate using the Hilbert–Huang Transform Method, J. Sens. Technol., 2015, vol. 5, no. 1, pp. 7–14.10.4236/jst.2015.51002 ↗
51. Xu, B., Wang, M., Li, P., et al., Application of instantaneous parameter characteristic in active Lamb wave based monitoring of plate structural health, Appl. Sci., 2020, vol. 10, no. 16, p. 5664.10.3390/app10165664 ↗
52. Li, Y.J., Zhang, X.C., Wang, J., et al., Underwater explosion of TNT dynamite with a metal shell and annular gap, 2005, vol. 9, no. 3, pp. 118–125.
53. De Rosset, W., Analysis of explosive bonding parameters, Mater. Manuf. Process., 2006, vol. 21, no. 6, pp. 634–638.10.1080/10426910600611136 ↗
54. Li, J., Song, S., Zhang, Y., et al., A Robust Fuzzy C-Means Clustering Algorithm for Incomplete Data, Singapore: Springer Singapore, 2017, pp. 3–12.10.1007/978-981-10-6373-2_1 ↗
55. Raghuram, V., Shukla, R., Pramila, T., et al., Studies on Lamb waves in long aluminium plates generated using laser based ultrasonics, 9th Int. Conf. Vib. Meas. Laser Non-Cont. Techn. Short Course (Ancona, 2010), vol. 1253, pp. 100–105.10.1063/1.3455446 ↗
56. Staszewski, W.J., Lee, B.C., Mallet, L., et al., Structural health monitoring using scanning laser vibrometry: I. Lamb wave sensing, Smart Mater. Struct., 2004, vol. 13, no. 2, pp. 251–260.10.1088/0964-1726/13/2/002 ↗
57. Mejjaoli, H., Dunkl–Stockwell transform and its applications to the time–frequency analysis, J. Pseudo-Differ. Oper. Appl., 2021, vol. 12, no. 2, p. 32.10.1007/s11868-021-00378-y ↗
58. Wu, Z. and Huang, N.E., Ensemble empirical mode decomposition: A noise-assisted data analysis method, Adv. Adapt. Data Anal., 2009, vol. 1, no. 1, pp. 1–41.10.1142/S1793536909000047 ↗
59. Yeh, J., Shieh, J., and Huang, N.E., Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method, Adv. Adapt. Data Anal., 2010, vol. 2, no. 2, pp. 135–156.10.1142/S1793536910000422 ↗
60. Torres, M.E., Colominas, M.A., Schlotthauer, G., et al., A complete ensemble empirical mode decomposition with adaptive noise, 2011 IEEE Int. Conf. Acoust. Speech Signal Proces. (ICASSP) (Prague, 2011).10.1109/ICASSP.2011.5947265 ↗