Defect identification using sampling and outlier analysis in passive guided wave structural health monitoring
Source abstract
Abstract
Passive Structural Health Monitoring (SHM) systems face a challenge in achieving control over the propagating noise field, thereby limiting their application towards defect identification and localization. To overcome this, an outlier statistical technique based on cross-correlation is developed and implemented here to achieve defect detection under passive loading. The proposed technique is first demonstrated using simulation and then validated using an experimental setup based on an air blower as the noise source imposing on an aluminium plate. The results clearly demonstrate the feasibility of defect detection using the sparse sampling-based outlier analysis in a passive SHM system that can effectively assess the structural health integrity of infrastructures deployed under harsh environmental conditions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Structural Health Monitoring Techniques · Concrete Corrosion and Durability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Center for Non-Destructive Evaluation and Department of Mechanical Engineering, Indian Institute of Technology, Madras, India
Department of Electrical Engineering, Indian Institute of Technology, Madras, India
References (36)
Show source-provided references
1. Ahmed S, Kopsaftopoulos F (2019) Uncertainty quantification of guided waves propagation for active sensing structural health monitoring. In Proceedings of the vertical flight society 75th annual forum & technology display, Philadelphia, PA, USA, pp. 13–16.
2. Bridson R (2007) Fast poisson disk sampling in arbitrary dimensions. SIGGRAPH Sketches 10(1): 1.
3. 10.1177/147592171880731010.1177/1475921718807310 ↗
4. 10.1177/147592172091499210.1177/1475921720914992 ↗
5. 10.1121/1.513406610.1121/1.5134066 ↗
6. 10.1063/1.486752210.1063/1.4867522 ↗
7. 10.1177/1045389X0809738610.1177/1045389X08097386 ↗
8. 10.1016/j.jsv.2014.05.01210.1016/j.jsv.2014.05.012 ↗
9. 10.1121/1.505401510.1121/1.5054015 ↗
10. 10.1177/147592171141423210.1177/1475921711414232 ↗
11. 10.1121/1.366539710.1121/1.3665397 ↗
12. 10.1016/j.ndteint.2021.10254310.1016/j.ndteint.2021.102543 ↗
13. 10.1088/1361-6501/ac261b10.1088/1361-6501/ac261b ↗
14. 10.1177/1475921721107333510.1177/14759217211073335 ↗
15. 10.1016/j.ijadhadh.2020.10276110.1016/j.ijadhadh.2020.102761 ↗
16. 10.1098/rspa.1917.000810.1098/rspa.1917.0008 ↗
17. 10.1063/1.173952910.1063/1.1739529 ↗
18. 10.1121/1.279991310.1121/1.2799913 ↗
19. 10.1063/1.365290710.1063/1.3652907 ↗
20. 10.1121/1.141752810.1121/1.1417528 ↗
21. Oppenheim AV, Willsky AS, Nawab SH (1997) Signals and Systems. Prentice-Hall.
22. 10.1016/j.compstruct.2018.08.02410.1016/j.compstruct.2018.08.024 ↗
23. 10.1007/978-1-4615-5947-4_2410.1007/978-1-4615-5947-4_24 ↗
24. Qadri BA, Tcherniak D, Ulriksen MD, et al. (2018) Damage detection in a reinforced concrete slab using outlier analysis. In 9th European Workshop on Structural Health Monitoring 3: 95.
25. 10.1109/ICICSP59554.2023.1039071510.1109/ICICSP59554.2023.10390715 ↗
26. 10.1016/j.ultras.2023.10717810.1016/j.ultras.2023.107178 ↗
27. 10.1121/10.000130010.1121/10.0001300 ↗
28. 10.1121/1.271046310.1121/1.2710463 ↗
29. Smith M (2009) Abaqus/Standard User’s Manual, Version 6.9. Providence, RI: Simulia.
30. Snieder R (2008) Extracting the green’s function from the correlation of coda waves: A derivation based on stationary phase. In Wapenaar K, Draganov D, Robertsson JO, et al. editors, Seismic Interferometry: History and Present Status 26: 305–316. Society of Exploration Geophysicists.
31. 10.1016/j.ndteint.2023.10289010.1016/j.ndteint.2023.102890 ↗
32. 10.1002/stc.269010.1002/stc.2690 ↗
33. 10.1006/jsvi.1999.251410.1006/jsvi.1999.2514 ↗
34. 10.1177/1045389X21107219710.1177/1045389X211072197 ↗
35. 10.1177/147592171774887810.1177/1475921717748878 ↗
36. 10.1177/147592171350110810.1177/1475921713501108 ↗