Original Article
  • High-Speed Defect Detection in Offshore Wind Turbine Blades Using Autoencoder-Based Thermographic Image Dataset
  • Haemyung Chon*, Taegyeong Jeong**, Jackyou Noh**†

  • * The Innovation Research Center for Giant Wind Turbine System, Kunsan National University
    ** Department of Naval Architecture, Kunsan National University

  • 해상풍력발전기 블레이드 열화상 데이터를 활용한 오토인코더 기반 고속 결함 탐지
  • 전해명* · 정태경** · 노재규**†

  • This article is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

References
  • 1. Ma, L., Jiang, X., Tang, Z., Zhi, S., and Wang, T., “Wind Turbine Blade Defect Detection Algorithm Based on Lightweight MES-YOLOv8n,” IEEE Sensors Journal, Vol. 24, No. 17, 2024, pp. 28409-28418.
  •  
  • 2. Chon, H., Oh, D., and Noh, J., “Enhanced Prediction Performance of Internal Defect Detection in Wind Turbine Blades on Thermography Using Deep Learning Models with Preprocessed Synthetic Data,” Applied Sciences, Vol. 15, No. 6, 2025, pp. 3042.
  •  
  • 3. Masita, K., Hasan, A. N., Shongwe, T., and Hilal, H. A., “Deep Learning in Defect Detection of Wind Turbine Blades: A Review,” IEEE Access, Vol. 13, 2025, pp. 98399-98425.
  •  
  • 4. Yang, B., and Sun, D., “Testing, Inspecting and Monitoring Technologies for Wind Turbine Blades: A Survey,” Renewable and Sustainable Energy Reviews, Vol. 22, 2013, pp. 515-526.
  •  
  • 5. Pratt, R., Allen, C., Masoum, M. A., and Seibi, A., “Defect Detection and Classification on Wind Turbine Blades Using Deep Learning with Fuzzy Voting,” Machines, Vol. 13, No. 4, 2025, pp. 283.
  •  
  • 6. Ibarra-Castanedo, C., Tarpani, J. R., and Maldague, X. P., “Nondestructive Testing with Thermography,” European Journal of Physics, Vol. 34, No. 6, 2013, pp. S91.
  •  
  • 7. Ibarra-Castanedo, C., Benítez, H., Maldague, X., and Bendada, A., “Review of Thermal-contrast-based Signal Processing Techniques for the Nondestructive Testing and Evaluation of Materials by Infrared Thermography,” In Proc. Int. Workshop on Imaging NDE, Kalpakkam, India, Apr. 2007, pp. 1-6.
  •  
  • 8. Kou, L., Li, Y., Zhang, F., Gong, X., Hu, Y., Yuan, Q., and Ke, W., “Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms,” Sensors, Vol. 22, No. 8, 2022, pp. 2822.
  •  
  • 9. Mishnaevsky Jr, L., “Root Causes and Mechanisms of Failure of Wind Turbine Blades: Overview,” Materials, Vol. 15, No. 9, 2022, pp. 2959.
  •  
  • 10. McGugan, M., Pereira, G., Sørensen, B. F., Toftegaard, H., and Branner, K., “Damage Tolerance and Structural Monitoring for Wind Turbine Blades,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 373, No. 2035, pp. 20140077.
  •  
  • 11. Kwaon, K. A., Choi, M. Y., Park, H. S., Park, J. H., Huh, Y. H., and Choi, W. J., “Quantitative Defects Detection in Wind Turbine Blade Using Optical Infrared Thermography,” Journal of the Korean Society for Nondestructive Testing, Vol. 35, No. 1, 2015, pp. 25-30.
  •  
  • 12. Kim, K. S., Jeon, S. Y., and Jung, H. C., “Defect Detection of Impacted Composite Tubes by Lock-in Photo-infrared Thermography Technique,” Journal of the Korean Society for Nondestructive Testing, Vol. 31, No. 2, 2011, pp. 139-143.
  •  
  • 13. Chon, H., and Noh, J., “Review of Wind Turbine Blade Defect Detection Algorithm Based on AI,” JMST Advances, Vol. 7, 2025, pp. 1-6.
  •  
  • 14. Ma, Y., Martinez-Vazquez, P., and Baniotopoulos, C., “Wind Turbine Tower Collapse Cases: A Historical Overview,” Proceedings of the Institution of Civil Engineers-Structures and Buildings, Vol. 172, No. 8, 2019, pp. 547-555.
  •  

This Article

Correspondence to

  • Jackyou Noh
  • Department of Naval Architecture, Kunsan National University

  • E-mail: snucurl@kunsan.ac.kr