Skip to main content
Springer Nature Link
Log in

Wind Turbine Surface Defect Detection Analysis from UAVs Using U-Net Architecture

  • Conference paper
  • First Online:
Intelligent Computing (SAI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 506))

Included in the following conference series:

  • 1037 Accesses

Abstract

Effective and timely identification of defects for wind turbine surface are crucial to propitiously repair and limit any further degradation. To date, the damage is detected via drones, on-site high-resolution photography, or physical examination, which is usually done after an annual rotor blade assessment. Obviously, any damage that goes unnoticed can worsen over time, raising the cost of repair and maintenance while also increasing the potential of catastrophic collapse. Maintenance after the discovery of a blade problem, or in the worst-case scenario, blade failure, can result in substantial repair and replacement expenses and significant revenue losses, especially in the case of offshore wind farms. If the damage had been discovered sooner, it could have been fixed for a far lower price. In this work, we propose an automated wind turbine surface distress analysis system based on the UNet deep learning framework. The system is trained using images acquired from UAVs and tested on several different datasets. The detection and classification accuracy of the proposed distress analyser is measured using the precision and recall values. Successful application of this study can help identify structure anomalies in need of urgent repair, thereby facilitating a much better civil infrastructure monitoring system. Experimental validation demonstrated the ability of the method to detect micro-crack locations with an accuracy of 96%. Experimental results are presented and discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Jiménez, A.A., Muñoz, C.Q.G., Márquez, F.P.G.: Machine learning for wind turbine blades maintenance management. Energies 11(1), 13 (2018)

    Article  Google Scholar 

  2. Chen, X., Yang, Y., Cui, Z., Shen, J.: Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy. Energy 174, 1100–1109 (2019)

    Article  Google Scholar 

  3. Doliński, L., Krawczuk, M., Żak, A.J.: Detection of delamination in laminate wind turbine blades using one-dimensional wavelet analysis of modal responses. Shock. Vib. 6, 1–15 (2018)

    Google Scholar 

  4. Dung, C.V., et al.: Autonomous concrete crack detection using deep fully convolutional neural network. Autom. Constr. 99, 52–58 (2019)

    Article  Google Scholar 

  5. Fan, R., et al.: Road crack detection using deep convolutional neural network and adaptive thresholding. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 474–479. IEEE (2019)

    Google Scholar 

  6. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE (2016)

    Google Scholar 

  7. Yong, H., Zhao, C.: A novel LBP based methods for pavement crack detection. J. Pattern Recogn. Res. 5(1), 140–147 (2010)

    Google Scholar 

  8. Iwicki, P., Przewłócki, J.: Short review and 3-D FEM analysis of basic types of foundation for offshore wind turbines. Pol. Marit. Res. 27, 31–39 (2020)

    Article  Google Scholar 

  9. Kim, H.-C., Kim, M.-H., Choe, D.-E.: Structural health monitoring of towers and blades for floating offshore wind turbines using operational modal analysis and modal properties with numerical-sensor signals. Ocean Eng. 188, 106226 (2019)

    Article  Google Scholar 

  10. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing System, pp. 1097–1105 (2012)

    Google Scholar 

  11. Lan, M., Zhang, Y., Zhang, L., Du, B.: Defect detection from UAV images based on region-based CNNs. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 385–390. IEEE (2018)

    Google Scholar 

  12. Laurent, J., Hébert, J.F., Talbot, M: Automated Detection of Sealed Cracks Using 2D and 3D Road Surface Data. Pavemetrics Systems Inc., Canada (2010)

    Google Scholar 

  13. Li, X., Yang, Q., Chen, Z., Luo, X., Yan, W.: Visible defects detection based on UAV-based inspection in large-scale photovoltaic systems. IET Renew. Power Gener. 11(10), 1234–1244 (2017)

    Article  Google Scholar 

  14. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440. IEEE (2015)

    Google Scholar 

  15. Luping, L., Mangmang, L.: The application of vibration detection technology in blade crack fault monitoring of wind turbine. J. Eng. Therm. Energy Power 28, 208–222 (2013)

    Google Scholar 

  16. Melhem, H., Kim, H.: Damage detection in concrete by Fourier and Wavelet analyses. J. Eng. Mech. 129(5), 571–577 (2003)

    Article  Google Scholar 

  17. Miraliakbari, A., Sok, S., Ouma, Y., Hahn, M.: Comparative evaluation of pavement crack detection using kernel-based techniques in asphalt road surfaces. Int. Arch. Photogram. Remote Sens. Spatial Inf. Sci. 1, 12–19 (2016)

    Google Scholar 

  18. Moll, J., et al.: Radar-based structural health monitoring of wind turbine blades: the case of damage detection. Struct. Health Monit. 17(4), 815–822 (2018)

    Article  MathSciNet  Google Scholar 

  19. Niezrecki, C., et al.: Inspection and monitoring of wind turbine blade-embedded wave defects during fatigue testing. Struct. Health Monit. 13(6), 629–643 (2014)

    Article  Google Scholar 

  20. Ozbek, M., Rixen, D.J., Erne, O., Sanow, G.: Feasibility of monitoring large wind turbines using photogrammetry. Energy 35(12), 4802–4811 (2010)

    Article  Google Scholar 

  21. Pang, Yu., Jia, L., Zhang, X., Liu, Z., Li, D.: Design and implementation of automatic fault diagnosis system for wind turbine. Comput. Electr. Eng. 87, 106754 (2020)

    Article  Google Scholar 

  22. Poozesh, P., Aizawa, K., Niezrecki, C., Baqersad, J., Inalpolat, M., Heilmann, G.: Structural health monitoring of wind turbine blades using acoustic microphone array. Struct. Health Monit. 16(4), 471–485 (2017)

    Article  Google Scholar 

  23. Salman, M., Mathavan, S., Kamal, K., Rahman, M.: Pavement crack detection using the Gabor filter. In: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp. 2039–2044. IEEE (2013)

    Google Scholar 

  24. Shahrokhinasab, E., Hosseinzadeh, N., Monirabbasi, A., Torkaman, S.: Performance of image-based crack detection systems in concrete structures. J. Soft Comput. Civ. Eng. 4(1), 127–139 (2020)

    Google Scholar 

  25. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  26. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9. IEEE (2015)

    Google Scholar 

  27. Teng, W., Ding, X., Cheng, H., Han, C., Liu, Y., Haihua, M.: Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform. Renew. Energy 136, 393–402 (2019)

    Article  Google Scholar 

  28. Toft, H.S., Sorensen, J.D.: Reliability-based design of wind turbine blades. Struct. Saf. 33(6), 333–342 (2011)

    Article  Google Scholar 

  29. Tong, Z., Yuan, D., Gao, J., Wang, Z.: Pavement defect detection with fully convolutional network and an uncertainty framework. Comput.-Aided Civ. Infrastruct. Eng. 35(8), 832–849 (2020)

    Article  Google Scholar 

  30. Wang, L., Zhang, Z.: Automatic detection of wind turbine blade surface cracks based on UAV-taken images. IEEE Trans. Ind. Electron. 64(9), 7293–7303 (2017)

    Article  Google Scholar 

  31. Wang, T., Gopalakrishnan, K., Somani, A.K., Smadi, O.G., Ceylan, H.: Machine-Vision-Based Roadway Health Monitoring and Assessment: Development of a Shape-Based Pavement-Crack-Detection Approach (2016)

    Google Scholar 

  32. Donghua, X., Wen, C., Liu, J.: Wind turbine blade surface inspection based on deep learning and UAV-taken images. J. Renew. Sustainable Energy 11(5), 053305 (2019)

    Article  Google Scholar 

  33. Zhang, L., Yang, F., Zhang, Y.D., Zhu, J.Z.: Road crack detection using deep convolutional neural network. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3708–3712. IEEE (2016)

    Google Scholar 

  34. Zhang, N., Lu, C.: Wind turbine blade defect image acquisition system. In: Journal of Physics: Conference Series, vol. 1646, p. 012086. IOP Publishing (2020)

    Google Scholar 

  35. Zhang, N., Lu, C., Wang, A.: Study on wind turbine blade defect detection system based on imaging array. In: E3S Web of Conferences, vol. 118, p. 02041. EDP Sciences (2019)

    Google Scholar 

  36. Zou, Q., Cao, Y., Li, Q., Mao, Q., Wang, S.: CrackTree: automatic crack detection from pavement images. Pattern Recogn. Lett. 33(3), 227–238 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Babylon, Babylon, Iraq

    Hassnaa Hasan Shaheed

  2. School of Mathematics and Physical Sciences, The University of Newcastle, Callaghan, NSW, 2308, Australia

    Riya Aggarwal

Authors
  1. Hassnaa Hasan Shaheed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Riya Aggarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Riya Aggarwal .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shaheed, H.H., Aggarwal, R. (2022). Wind Turbine Surface Defect Detection Analysis from UAVs Using U-Net Architecture. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-031-10461-9_34

Download citation

Publish with us

Policies and ethics