journal article·2025-12

Detection and Classification of Aviation Cable Insulation Defects Using Digital Holography and Deep Learning

Athira Shaji, Sheeja M. K.

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OpenAlex topics: Power Line Inspection Robots · Belt Conveyor Systems Engineering · Non-Destructive Testing Techniques

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References (44)

Show source-provided references
1. Jiang, J., Li, Z., Li, W., et al.: A review on insulation challenges towards electrification of aircraft. High Voltage. 8(2), 209–230 (2023)10.1049/hve2.12304 ↗
2. Wang, Q., Yao, G., Yu, X. et al.: Predicting the fatigue survival probability of High-Strength steel Wires: A Weibull model incorporating corrosion and stress ratios. Eng. Fail. Anal. 167, 109063 (2025). ISSN 1350-630710.1016/j.engfailanal.2024.109063 ↗
3. Waqar, M., Memon, A.M., Sabih, M., Alhems, L.M.: Composite pipelines: Analyzing defects and advancements in nondestructive testing techniques. Eng. Fail. Anal. 157, 107914 (2024). ISSN 1350-630710.1016/j.engfailanal.2023.107914 ↗
4. Vavilova, G., Ryumkin, A.: Detection of insulation defects in the wire through measuring changes in its capacitance. IOP Conference Series: Mater. Sci. Eng. 289, 012017 (2018). https://doi.org/10.1088/1757-899X/289/1/01201710.1088/1757-899X/289/1/012017 ↗
5. Wen, F., Jin, J.: Detection of superficial defects in the insulation layers of aviation cables by infrared thermographic technique. Coatings 12(6), 745 (2022)10.3390/coatings12060745 ↗
6. Li, X., Zhang, J., Shi, J.: Quantitative Nondestructive Testing of Broken Wires for Wire Rope Based on Magnetic and Infrared Information. J. Sensors 2020(14), 6419371 (2020)10.1155/2020/6419371 ↗
7. Qu, Z., Zhang, Q., Fu, Y., An, Y., Chen, C., Shan, X., Sun, Y., Zhao, J.: Defect detection for the insulation layer of bent aircraft cables based on ultrasonic guided waves. IEEE Trans. Instrum. Meas. 72, 1–8 (2023)
8. Qu, Z., Fu, Y., Zhang, Q., An, Y., Shan, X., Sun, Y., Zhao, J., Chen, C.: Structural health monitoring for multi-strand aircraft wire insulation layer based on ultrasonic guided waves. Appl. Acoust. 201, 109109 (2022)10.1016/j.apacoust.2022.109109 ↗
9. Zhao, N., Duan, Z., Li, Q., Guo, K., Zhang, Z., Liu, B.: A cable insulation defect classification method based on cnn-transformer. Front. Phys. 12, 1432527 (2024)10.3389/fphy.2024.1432527 ↗
10. Qiu, M., Ge, X.: Nondestructive evaluation of aging failure in insulation coatings by ultrasonic guided wave based on signal processing and machine learning. Coatings 15, 347 (2025)10.3390/coatings15030347 ↗
11. Zhou, P., Zhou, G., Zhu, Z., He, Z., Ding, X., Tang, C.: A review of non-destructive damage detection methods for steel wire ropes. Appl. Sci. 9, 2771 (2019)10.3390/app9132771 ↗
12. Tsai, D.M., Jen, P.H.: Autoencoder-based anomaly detection for surface defect inspection. Adv. Eng. Inf. 48, 101272 (2021)10.1016/j.aei.2021.101272 ↗
13. Getachew Shiferaw, T., Yao, L.: Autoencoder-Based Unsupervised Surface Defect Detection Using Two-Stage Training. J. Imaging 10, 111 (2024)10.3390/jimaging10050111 ↗
14. Wang, Q., Huang, M., Wang, X., Zhang, Y., Zhang, Q., An, Y., Wang, C., Qu, Z.: Defect detection and identification for aircraft cable insulation layer based on deep forest. Ultrasonics 135, 107128 (2023)10.1016/j.ultras.2023.107128 ↗
15. Zhou, T., Zhu, X., Yang, H., Yan, X., Jin, X., Wan, Q.: Identification of xlpe cable insulation defects based on deep learning. Global Energy Interconnection 6(1), 36–49 (2023)10.1016/j.gloei.2023.02.004 ↗
16. Liu, S., Chen, M.: Wire rope defect recognition method based on mfl signal analysis and 1d-cnns. Sensors 23, 3366 (2023)10.3390/s23073366 ↗
17. Zhang, G., Tang, Z., Fan, Y., Liu, J., Jahanshahi, H., Aly, A.A.: Steel wire rope surface defect detection based on segmentation template and spatiotemporal gray sample set. Sensors 21, 5401 (2021)10.3390/s21165401 ↗
18. Guo, X., Liu, X., Long, G., Zhao, Y., Yuan, Y.: Data-driven prediction of the fatigue performance of corroded high-strength steel wires. Eng. Fail. Anal. 146, 107108 (2023). ISSN 1350-630710.1016/j.engfailanal.2023.107108 ↗
19. Wang, Q., Yao, G., Kong, G., Wei, L., Yu, X., Jianchuan, Z., Ran, C., Luo, L.: A data-driven model for predicting fatigue performance of high-strength steel wires based on optimized XGBOOST. Eng. Fail. Anal. 164, 108710 (2024). ISSN 1350-630710.1016/j.engfailanal.2024.108710 ↗
20. Utadiya, S., Trivedi, V. et.al.: Digital holographic imaging of thermal signatures and its use in inhomogeneity identification. Optics Lasers Eng. 160, 107227 (2023)10.1016/j.optlaseng.2022.107227 ↗
21. Li, H., Chen, X., Chi, Z., Mann, C., Razi, A.: Deep dih: single-shot digital in-line holography reconstruction by deep learning. IEEE Access 8, 202648–202659 (2020). https://doi.org/10.1109/ACCESS.2020.303638010.1109/ACCESS.2020.3036380 ↗
22. Zhu, Y., Hang Yeung, C., Lam, E.Y.: Digital holographic imaging and classification of microplastics using deep transfer learning. Appl. Opt. 60, A38–A47 (2021)10.1364/AO.403366 ↗
23. Thomas, L., Sheeja, M.K.: Digital holographic technique based breast cancer detection using transfer learning method. J. Biophoton. e202200359 (2023)10.1002/jbio.202200359 ↗
24. Luo, H., Xu, J., Jiao, J., Zhong, L., Lu, X., Tian, J.: Moment-Based Shape-Learning Holography for Fast Classification of Microparticles. Adv. Photon. Res. 4 (2023). https://doi.org/10.1002/adpr.20237001910.1002/adpr.202370019 ↗
25. Park, D., Park, J.: Improving the quality of light-field data extracted from a hologram using deep learning. ETRI J. 1–10 (2023)10.4218/etrij.2022-0441 ↗
26. Wittwer, F., Hagemann, J., Brückner, D., Flenner, S., Schroer, C.G.: Phase retrieval framework for direct reconstruction of the projected refractive index applied to ptychography and holography. Optica 9(3), 295–302 (2022)10.1364/OPTICA.447021 ↗
27. Huhn, S.: Leon merten lohse, jens lucht, and tim salditt, “fast algorithms for nonlinear and constrained phase retrieval in near-field x-ray holography based on tikhonov regularization.” Opt. Express 30, 32871–32886 (2022)10.1364/OE.462368 ↗
28. Bian, L., Wang, X., Li, D., Ren, Q., Zheng, D.: Robust phase unwrapping via non-local regularization. Opt. Lett. 48, 1399–1402 (2023)10.1364/OL.478603 ↗
29. Latychevskaia, T.: Iterative phase retrieval for digital holography: tutorial. J. Opt. Soc. Am. A 36, D31–D40 (2019)10.1364/JOSAA.36.000D31 ↗
30. Latychevskaia, T.: Reconstruction of missing information in diffraction patterns and holograms by iterative phase retrieval. Opt. Commun. 452, 56–67 (2019)10.1016/j.optcom.2019.07.021 ↗
31. de Almeida, J.L., Comunello, E., Sobieranski, A., et al.: Twin-image suppression in digital in-line holography based on wave-front filtering. Pattern. Anal. Applic. 24, 907–914 (2021)10.1007/s10044-020-00949-7 ↗
32. Xuelian, Yu., Ban, J., Xiao, J., Li, X., Wang, K., Jiajing, Yu., Bao, X.: Efficient compressive holographic reconstruction based on hologram segmentation. Appl. Opt. 60, 9844–9851 (2021)10.1364/AO.434813 ↗
33. de Souza, J.C., Freire, R.B.R., dos Santos, P.A.M.: Subsampled digital holographic image reconstruction by a compressive sensing approach. Appl. Opt. 60, 1–9 (2021)10.1364/AO.405298 ↗
34. Zhang, H., Liu, S., Cao, L., Brady, D.J.: Noise suppression for ballistic-photons based on compressive in-line holographic imaging through an inhomogeneous medium. Opt. Express 28, 10337–10349 (2020)10.1364/OE.385992 ↗
35. Kumar, S., Mahadevappa, M., Dutta, P.K.: Compressive holography from poisson noise plagued holograms using expectation-maximization. IEEE Trans. Computat. Imaging 6, 857–867 (2020)10.1109/TCI.2020.2984411 ↗
36. Birdi, J., Rajora, S., Butola, M., Khare, K.: True 3D reconstruction in digital holography. J. Phys.: Photon. 2(4) (2020)10.1088/2515-7647/abb586 ↗
37. Gao, Y., Cao, L.: Iterative projection meets sparsity regularization: towards practical single-shot quantitative phase imaging with in-line holography. Light: Adv. Manufact. 4, 6 (2023)10.37188/lam.2023.006 ↗
38. Xu, D., Huang, Z., Cao, L.: Adaptive constraints by morphological operations for single-shot digital holography. Sci. Rep. 13, 10267 (2023)10.1038/s41598-023-37423-3 ↗
39. Sun, D., Luo, Z., Ping, S., Ma, J., Cao, L.: Influence of sparse constraint functions on compressive holographic tomography. Appl. Opt. 60, A111–A119 (2021)10.1364/AO.404341 ↗
40. Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. now (2011)10.1561/9781601984616 ↗
41. TE Connectivity. (n.d.). Datasheet for product 2160123004. Retrieved from https://www.te.com/en/product-2160123004.datasheet.pdf
42. Wang, M., Liu, Y., Huang, Y., et al.: Defect identification of xlpe power cable using harmonic visualized characteristics of grounding current. Electronics 13(6), 1159 (2024 Mar 21)10.3390/electronics13061159 ↗
43. Wang, Y., Xu, H., Wang, A., Huang, K., et al.: Cable insulation defect prediction based on harmonic anomaly feature analysis. Electronics 13(19), 3807–7 (2024 Sep 26)10.3390/electronics13193807 ↗
44. Hu, R., Xu, H., Lu, X., Wang, A., Xu, Z., Wang, Y., et al.: Research on live detection technology of distribution network cable insulation deterioration state based on harmonic components. IET Gener. Transm. Distrib. 18(17), 2847–59 (2024)10.1049/gtd2.13238 ↗