A novel characteristic slope-based defect depth inversion method for pipelines using the flexible honeycomb-shaped eddy current sensor
Source abstract
Abstract
Eddy current testing is critical for industrial pipeline safety. The measurement of defect depth on curved surfaces is influenced by both defect orientations and lengths. In this paper, a novel characteristic slope-based defect depth estimation method is proposed using the designed flexible honeycomb-shaped sensor. The designed flexible honeycomb-shaped sensor with the adjacent anti-phase excitation strategy can enhance magnetic field strength and suppress lift-off noise. From the detected signals, the signal trajectories in the complex plane exhibit a slope invariance with respect to defect orientations and lengths, while maintaining a distinct monotonic dependency on defect depth. Based on this slope feature, a defect depth inversion model is constructed employing the Natural Gradient Boosting algorithm. Experiments have been conducted on aluminium alloy pipes containing defects of varying lengths and orientations. The results demonstrate that the method effectively overcomes the interference of defect orientations and lengths. It achieves high precision depth assessment with an average prediction accuracy reaching 98.8%.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Non-Destructive Testing Techniques · Ultrasonics and Acoustic Wave Propagation · Electrical and Bioimpedance Tomography
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Fuzhou University
The University of Manchester
Funding
National Natural Science Foundation of China
References (30)
Show source-provided references
1. 10.1016/B978-0-12-824359-6.00024-X10.1016/B978-0-12-824359-6.00024-X ↗
2. 10.1007/s11665-022-06799-010.1007/s11665-022-06799-0 ↗
3. 10.1016/j.ndteint.2023.10303010.1016/j.ndteint.2023.103030 ↗
4. 10.1016/j.ultras.2020.10622710.1016/j.ultras.2020.106227 ↗
5. 10.1016/j.matpr.2017.11.62010.1016/j.matpr.2017.11.620 ↗
6. 10.1088/1742-6596/2467/1/01203010.1088/1742-6596/2467/1/012030 ↗
7. 10.1016/j.ndteint.2017.02.00110.1016/j.ndteint.2017.02.001 ↗
8. 10.1016/j.sna.2019.05.02610.1016/j.sna.2019.05.026 ↗
9. 10.1109/TIM.2025.356682310.1109/TIM.2025.3566823 ↗
10. 10.1177/002029401880138210.1177/0020294018801382 ↗
11. 10.3390/s2417581910.3390/s24175819 ↗
12. 10.1016/j.sna.2020.11184410.1016/j.sna.2020.111844 ↗
13. 10.1080/10589759.2024.238166710.1080/10589759.2024.2381667 ↗
14. 10.1080/10589759.2025.260994410.1080/10589759.2025.2609944 ↗
15. 10.1109/JSEN.2021.309355010.1109/JSEN.2021.3093550 ↗
16. 10.1109/FENDT.2014.692826310.1109/FENDT.2014.6928263 ↗
17. 10.3390/s15122991110.3390/s151229911 ↗
18. 10.3390/s1607095210.3390/s16070952 ↗
19. 10.1088/1361-6501/aa5b7610.1088/1361-6501/aa5b76 ↗
20. 10.1016/j.measurement.2025.11672210.1016/j.measurement.2025.116722 ↗
21. 10.1016/j.ymssp.2025.11299010.1016/j.ymssp.2025.112990 ↗
22. 10.1049/iet-cds.2016.032710.1049/iet-cds.2016.0327 ↗
23. 10.1063/1.354040910.1063/1.3540409 ↗
24. 10.1016/j.ndteint.2012.07.00410.1016/j.ndteint.2012.07.004 ↗
25. 10.1016/j.measurement.2012.09.01410.1016/j.measurement.2012.09.014 ↗
26. 10.1016/j.jmmm.2020.16641210.1016/j.jmmm.2020.166412 ↗
27. 10.1080/10589759.2013.82360810.1080/10589759.2013.823608 ↗
28. 10.1109/TIM.2024.341810110.1109/TIM.2024.3418101 ↗
29. 10.1109/TMAG.2009.203827510.1109/TMAG.2009.2038275 ↗
30. Duan T. Ngboost: natural gradient boosting for probabilistic prediction. In: 37th International Conference on Machine Learning (ICML 2020). PMLR; 2020. p. 2690–2700.