Non-destructive testing of composite materials with ultrasonic array based on EMD-ATRM
Source abstract
Abstract
The Lamb wave-based damage imaging technique offers a novel approach for structural health monitoring. However, enhancing the accuracy of damage detection continues to pose a significant challenge. To improve the precision and reliability of defect detection and localisation in composite plates, this paper introduces and investigates the Empirical Mode Decomposition-based Adaptive Time Reversal Method (EMD-ATRM). This method integrates adaptive Tukey window inverse filtering with a data-driven advanced technique, empirical mode decomposition, to effectively mitigate noise and accurately extract the primary modes. Moreover, the enhanced time reversal method is coupled with the Improved Reconstruction Algorithm for Probabilistic Inspection of Damage (IRAPID) to achieve precise computation of damage index values and efficient execution of time-reversed imaging. Experimental results demonstrate that, compared to traditional Time Reversal Method (TRM), EMD-ATRM exhibits superior defect localisation accuracy, clearer damage images, a reduced false alarm rate, and enhanced capability in identifying damage paths and regions. This study not only provides innovative methods and tools for the structural health monitoring of composite materials but also highlights the potential applications of EMD-ATRM in industrial settings, guiding future research directions.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Geophysical Methods and Applications · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Guizhou University
Funding
National Key R&D Program · GHB Platform Talent Project of Guizhou Science and Technology Department · National Natural Science Foundation of China · Education Department of Guizhou Province Project · Open Fund of the National Laboratory of Big Data for Public Safety
References (48)
Show source-provided references
1. 10.1016/j.matpr.2022.01.01910.1016/j.matpr.2022.01.019 ↗
2. 10.13801/j.cnki.fhclxb.20231212.00210.13801/j.cnki.fhclxb.20231212.002 ↗
3. 10.1016/j.cja.2019.09.01710.1016/j.cja.2019.09.017 ↗
4. 10.1016/j.matdes.2022.11099410.1016/j.matdes.2022.110994 ↗
5. 10.1016/j.cemconcomp.2019.10349810.1016/j.cemconcomp.2019.103498 ↗
6. 10.1016/j.cscm.2022.e0127110.1016/j.cscm.2022.e01271 ↗
7. 10.1002/stc.191910.1002/stc.1919 ↗
8. 10.1016/j.compstruct.2018.01.09610.1016/j.compstruct.2018.01.096 ↗
9. 10.1088/1361-665X/ac545510.1088/1361-665X/ac5455 ↗
10. 10.1016/j.measurement.2020.10750410.1016/j.measurement.2020.107504 ↗
11. 10.3390/jcs401001310.3390/jcs4010013 ↗
12. 10.1177/147592172094500010.1177/1475921720945000 ↗
13. 10.1016/j.compositesa.2017.06.01810.1016/j.compositesa.2017.06.018 ↗
14. 10.1002/adfm.20200361910.1002/adfm.202003619 ↗
15. 10.1117/12.48349210.1117/12.483492 ↗
16. 10.1016/j.wavemoti.2015.05.01010.1016/j.wavemoti.2015.05.010 ↗
17. 10.1016/j.ymssp.2019.10649110.1016/j.ymssp.2019.106491 ↗
18. 10.1088/0964-1726/15/4/00710.1088/0964-1726/15/4/007 ↗
19. 10.1016/j.prostr.2019.08.10310.1016/j.prostr.2019.08.103 ↗
20. 10.3390/s2322905010.3390/s23229050 ↗
21. 10.1088/0034-4885/63/12/20210.1088/0034-4885/63/12/202 ↗
22. 10.1109/58.15617410.1109/58.156174 ↗
23. 10.1088/0964-1726/22/10/10502610.1088/0964-1726/22/10/105026 ↗
24. 10.1039/C8EE02656D10.1039/C8EE02656D ↗
25. 10.1088/1361-665X/ab802810.1088/1361-665X/ab8028 ↗
26. Chang J, Jin M, Zeng J, et al. Analysis on time reversal imaging in presence of random media. J Univ Chin Acad Sci. 2018;35:59–65.
27. 10.1016/j.ultras.2021.10660610.1016/j.ultras.2021.106606 ↗
28. 10.1155/2014/94967110.1155/2014/949671 ↗
29. Calomfirescu M. Lamb waves for structural health monitoring in viscoelastic composite materials. 2008. https://api.semanticscholar.org/CorpusID:3178128
30. 10.1016/j.ymssp.2022.10930510.1016/j.ymssp.2022.109305 ↗
31. 10.1177/147592171452126810.1177/1475921714521268 ↗
32. 10.1177/1045389X1454987010.1177/1045389X14549870 ↗
33. 10.1002/lpor.20170018310.1002/lpor.201700183 ↗
34. 10.1109/ICASSP.2011.594726510.1109/ICASSP.2011.5947265 ↗
35. 10.1016/j.ijforecast.2021.12.01310.1016/j.ijforecast.2021.12.013 ↗
36. 10.1016/j.energy.2016.02.09810.1016/j.energy.2016.02.098 ↗
37. 10.1016/j.energy.2022.12540710.1016/j.energy.2022.125407 ↗
38. 10.1016/j.xcrp.2022.10091810.1016/j.xcrp.2022.100918 ↗
39. 10.11936/bjutxb201709001310.11936/bjutxb2017090013 ↗
40. 10.1109/TUFFC.2005.156168110.1109/TUFFC.2005.1561681 ↗
41. 10.1088/1674-1056/ab81f710.1088/1674-1056/ab81f7 ↗
42. 10.1134/S106183092270002410.1134/S1061830922700024 ↗
43. 10.1016/j.measurement.2023.11318210.1016/j.measurement.2023.113182 ↗
44. 10.1088/0964-1726/16/4/03210.1088/0964-1726/16/4/032 ↗
45. 10.3390/s2003086010.3390/s20030860 ↗
46. 10.1016/j.compstruct.2019.03.00810.1016/j.compstruct.2019.03.008 ↗
47. 10.1088/1361-665X/ac726f10.1088/1361-665X/ac726f ↗
48. Luo K, Chen L, Liang W, et al. A dual-scale morphological filtering method for composite damage identification using FBP. Mech Syst Signal Process. n.d.