Year 2025,
Volume: 9 Issue: 2, 89 - 95, 20.08.2025
Mehmet Bahadır Çetinkaya
,
Murat Gürek
References
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1. Doukas, H., C. Karakosta, A. Flamos, and J. Psarras, Electric power transmission: An overview of associated burdens. International Journal of Energy Research, 2011. 35(11): p. 979-988.
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2. Powers, W.F. The basics of power cable. IEEE Transactions on Industry Applications, 1994. 30(3): p. 506-509.
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3. Vaid, K. A polymer studies for insulated power cable. Asian Journal for Convergence in Technology, 2018. 4(1): p. 2350-1146.
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4. Uydur, C.C., O. Arıkan and O. Kalenderli, The effect of insulation defects on electric field distribution of power cable, in 2018 IEEE International Conference on High Voltage Engineering and Application.. 2018. Athens, Greece: p. 1-4.
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5. Siirto, O., J. Vepsäläinen, A. Hämäläinen, and M. Loukkalahti, Improving reliability by focusing on the quality and condition of medium-voltage cables and cable accessories. The Institution of Engineering and Technology, 2017. p. 229-232.
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6. Zhou, M., T. Wu, Z. Xia, B. He, L.B. Kong and H. Su. Research progress in deep learning for ceramics surface defect detection. Measurement, 2025. 242, Article ID: 115956.
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7. Li, Z.Y., B.H. Kwan, M.L. Tham, O.E. Ng and P.S.P. Wang. Abnormal detection of commutator surface defects based on YOLOv8. International Journal of Pattern Recognition and Artificial Intelligence, 2024. 38(12), Article ID: 2450013.
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8. Han, J., G. Cui, Z. Li, and J. Zhao. DBCW-YOLO: A modified YOLOv5 for the detection of steel surface defects. Applied Sciences-Basel, 2024. 14(11), Article ID: 4594.
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9. Ye, Q., Y. Dong, X. Zhang, D. Zhang, and S. Wang, Robustness defect detection: Improving the performance of surface defect detection in interference environment. Optics and Lasers in Engineering, 2024. 175, Article ID: 108035.
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10. Song, X., S. Cao, J. Zhang and Z. Hou. Steel surface defect detection algorithm based on YOLOv8. Electronics, 2024. 13(5), Article ID: 988.
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14. Ma, Z., Y. Li, M. Huang, Q. Huang, J. Cheng and S. Tang, Automated real-time detection of surface defects in manufacturing processes of aluminum alloy strip using a lightweight network architecture. Journal of Intelligent Manufacturing, 2023. 34(5): p. 2431-2447.
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15. Zhuxi, M. A., Y. Li, M. Huang, Q. Huang, J. Cheng and S. Tang, A lightweight detector based on attention mechanism for aluminum strip surface defect detection. Computers in Industry, 2022. 136, Article ID: 103585.
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16. Wang, M., L. Yang, Z. Zhao and Y. Guo, Intelligent prediction of wear location and mechanism using image identification based on improved Faster R-CNN model. Tribology International, 2022. 169, Article ID: 107466.
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17. Kim, B., M. Shin and S. Hwang, Design and development of a precision defect detection system based on a line scan camera using deep learning. Applied Sciences-Basel, 2024. 14(24), Article ID: 12054.
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18. Cui, W., K. Song, X. Jia, H. Chen, Y. Zhang, Y. Yan and W. Jiang, An efficient targeted design for real-time defect detection of surface defects. Optics and Lasers in Engineering, 2024. 178, Article ID: 108174.
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19. Cheng, J., G. Wen, X. He, X. Liu, Y. Hu and S. Mei, Achieving the defect transfer detection of semiconductor wafer by a novel prototype learning-based semantic segmentation network. IEEE Transactions on Instrumentation and Measurement, 2024. 73, Article ID: 5002212.
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20. Carrilho, R., K.A. Hambarde and H. Proença, A novel dataset for fabric defect detection: Bridging gaps in anomaly detection. Applied Sciences-Basel, 2024. 14(12), Article ID: 5298.
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21. Liu, Q., D. He, Y. Shen, Z. Lao, R. Ma and J. Li. Surface defect detection of stay cable sheath based on autoencoder and auxiliary anomaly location. Advanced Engineering Informatics, 2024. 62(2024), Article ID: 102759.
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22. Krol, K., G. Klosowski, M. Kulisz and A. Malek. Developing an expert system for analyzing defects on production lines. Przeglad Elektrotechniczny, 2025. 1(1): p. 187-190.
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23. Anton, J.C.M., P. Siegmann, L.M.S. Brea, E. Bernabeu, J.A.G. Pedrero and H.A. Canabal, In-line detection and evaluation of surface defects on thin metallic wires. Proceedings of the Optical Measurement Systems for Industrial Inspection II: Applications in Production Engineering, 2001. Munich: p. 27-34.
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24. Vijayakumar, A. and S. Vairavasundaram, Yolo-based object detection models: A review and its applications. Multimedia Tools and Applications, 2024. 83, p. 83535–83574.
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25. Ivsimaging. [cited 2025 07 April]; Available from: https://www.ivsimaging.com / Products / Cameras / The Imaging - Source-DFK-22BUC03.
Prototype line design for real-time detection of surface defects occurring on power cables
Year 2025,
Volume: 9 Issue: 2, 89 - 95, 20.08.2025
Mehmet Bahadır Çetinkaya
,
Murat Gürek
Abstract
The optimization of quality control processes in real-time industrial systems is crucial for production efficiency. After obtaining the instantaneous data from the production line, the recording, transmitting and reporting of the relevant data with high accuracy will provide significant advantages in terms of efficiency and product quality. In this study, a prototype line including rotating reel system was designed for the real-time detection of defects that may occur on power cable surfaces during the production process. The prototype line was designed so as to operate in the speed range of [0, 1 m/s] and different defect types occurring on the cable surfaces were detected via a lighted closed camera box located on the prototype line. From the results obtained, it was observed that real-time datasets could be created with high accuracy and diversity through the prototype line developed and the datasets created could successfully be used in the modelling of industrial systems.
Thanks
This study was supported by Erciyes University Scientific Research Projects Unit with the project of “FDK-2024-14010”. This study was also carried out within the R&D Center of Hasçelik Cable Ind. & Trade. Corp.
References
-
1. Doukas, H., C. Karakosta, A. Flamos, and J. Psarras, Electric power transmission: An overview of associated burdens. International Journal of Energy Research, 2011. 35(11): p. 979-988.
-
2. Powers, W.F. The basics of power cable. IEEE Transactions on Industry Applications, 1994. 30(3): p. 506-509.
-
3. Vaid, K. A polymer studies for insulated power cable. Asian Journal for Convergence in Technology, 2018. 4(1): p. 2350-1146.
-
4. Uydur, C.C., O. Arıkan and O. Kalenderli, The effect of insulation defects on electric field distribution of power cable, in 2018 IEEE International Conference on High Voltage Engineering and Application.. 2018. Athens, Greece: p. 1-4.
-
5. Siirto, O., J. Vepsäläinen, A. Hämäläinen, and M. Loukkalahti, Improving reliability by focusing on the quality and condition of medium-voltage cables and cable accessories. The Institution of Engineering and Technology, 2017. p. 229-232.
-
6. Zhou, M., T. Wu, Z. Xia, B. He, L.B. Kong and H. Su. Research progress in deep learning for ceramics surface defect detection. Measurement, 2025. 242, Article ID: 115956.
-
7. Li, Z.Y., B.H. Kwan, M.L. Tham, O.E. Ng and P.S.P. Wang. Abnormal detection of commutator surface defects based on YOLOv8. International Journal of Pattern Recognition and Artificial Intelligence, 2024. 38(12), Article ID: 2450013.
-
8. Han, J., G. Cui, Z. Li, and J. Zhao. DBCW-YOLO: A modified YOLOv5 for the detection of steel surface defects. Applied Sciences-Basel, 2024. 14(11), Article ID: 4594.
-
9. Ye, Q., Y. Dong, X. Zhang, D. Zhang, and S. Wang, Robustness defect detection: Improving the performance of surface defect detection in interference environment. Optics and Lasers in Engineering, 2024. 175, Article ID: 108035.
-
10. Song, X., S. Cao, J. Zhang and Z. Hou. Steel surface defect detection algorithm based on YOLOv8. Electronics, 2024. 13(5), Article ID: 988.
-
11. Zhang, Y., H. Xinbo, J. Jia and X Liu, A recognition technology of transmission lines conductor break and surface damage based on aerial image. IEEE Access, 2019. 7: p. 59022-59036.
-
12. Bükücü, Ç.C., and L. Gökrem, A new prototype that performs real-time error detection in glass products. International Journal of Engineering Research and Development, 2020. 12(2): p. 510-519.
-
13. Liu, Y., J. Wnag, H. Yu, J. Li, F. Li, and Q. Zhao, A non-invasive system for on-line surface defect detection on special-shaped steel towards real production lines. IEEE International Instrumentation and Measurement Technology Conference, 2022. Canada: p. 1-6.
-
14. Ma, Z., Y. Li, M. Huang, Q. Huang, J. Cheng and S. Tang, Automated real-time detection of surface defects in manufacturing processes of aluminum alloy strip using a lightweight network architecture. Journal of Intelligent Manufacturing, 2023. 34(5): p. 2431-2447.
-
15. Zhuxi, M. A., Y. Li, M. Huang, Q. Huang, J. Cheng and S. Tang, A lightweight detector based on attention mechanism for aluminum strip surface defect detection. Computers in Industry, 2022. 136, Article ID: 103585.
-
16. Wang, M., L. Yang, Z. Zhao and Y. Guo, Intelligent prediction of wear location and mechanism using image identification based on improved Faster R-CNN model. Tribology International, 2022. 169, Article ID: 107466.
-
17. Kim, B., M. Shin and S. Hwang, Design and development of a precision defect detection system based on a line scan camera using deep learning. Applied Sciences-Basel, 2024. 14(24), Article ID: 12054.
-
18. Cui, W., K. Song, X. Jia, H. Chen, Y. Zhang, Y. Yan and W. Jiang, An efficient targeted design for real-time defect detection of surface defects. Optics and Lasers in Engineering, 2024. 178, Article ID: 108174.
-
19. Cheng, J., G. Wen, X. He, X. Liu, Y. Hu and S. Mei, Achieving the defect transfer detection of semiconductor wafer by a novel prototype learning-based semantic segmentation network. IEEE Transactions on Instrumentation and Measurement, 2024. 73, Article ID: 5002212.
-
20. Carrilho, R., K.A. Hambarde and H. Proença, A novel dataset for fabric defect detection: Bridging gaps in anomaly detection. Applied Sciences-Basel, 2024. 14(12), Article ID: 5298.
-
21. Liu, Q., D. He, Y. Shen, Z. Lao, R. Ma and J. Li. Surface defect detection of stay cable sheath based on autoencoder and auxiliary anomaly location. Advanced Engineering Informatics, 2024. 62(2024), Article ID: 102759.
-
22. Krol, K., G. Klosowski, M. Kulisz and A. Malek. Developing an expert system for analyzing defects on production lines. Przeglad Elektrotechniczny, 2025. 1(1): p. 187-190.
-
23. Anton, J.C.M., P. Siegmann, L.M.S. Brea, E. Bernabeu, J.A.G. Pedrero and H.A. Canabal, In-line detection and evaluation of surface defects on thin metallic wires. Proceedings of the Optical Measurement Systems for Industrial Inspection II: Applications in Production Engineering, 2001. Munich: p. 27-34.
-
24. Vijayakumar, A. and S. Vairavasundaram, Yolo-based object detection models: A review and its applications. Multimedia Tools and Applications, 2024. 83, p. 83535–83574.
-
25. Ivsimaging. [cited 2025 07 April]; Available from: https://www.ivsimaging.com / Products / Cameras / The Imaging - Source-DFK-22BUC03.