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Few-shot wind turbine blade damage early warning system based on sound signal fusion

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Abstract

Wind energy is one of the fastest-growing renewable energy resources. The blades are regarded as one of the most critical components in a wind turbine. The appropriate detection scheme to ensure the safety of the blade is crucial. Although there are many ways to detect blade damage and distinguish the types of them, a real-time online blade alert is important to ensure that potential wind turbine problems can be corrected in a timely manner. In this paper, a wind turbine blade damage early warning system was designed and developed based on sound signal fusion. Firstly, a wind turbine blade early warning method based on wavelet packet decomposition is proposed, which mainly includes data processing, feature extraction and early warning mechanism. Specifically, the beamforming technology of minimum variance distortion-less response(MVDR) is applied to enhance the weak signal and suppress the interference signal in the data processing. In the feature extraction, four-layer wavelet packet decomposition is applied to fully retain the information in the original signal. To improve the robustness of early warning, the two early warning strategies are introduced into in early warning mechanism. Then, a wind turbine blade damage early warning system was developed based on specific hardware. Finally, the system is tested on active wind farms and can achieve good early warning results.

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References

  1. Kazmerski, L.: Renewable and sustainable energy reviews. Renew. Sustain. Energy Rev. 38, 834–847 (2016). https://doi.org/10.1016/j.rser.2014.07.023

    Article  Google Scholar 

  2. Yang, W., Crabtree, C.J.: Cost-effective condition monitoring for wind turbines. IEEE Trans. Ind. Electron. 57, 263–271 (2010). https://doi.org/10.1088/0964-1726/24/3/033001

    Article  Google Scholar 

  3. Keatley, P., Shibli, A., Hewitt, N.J.: Estimating power plant start cost sincyclic operation. Appl. Energy 111, 550–557 (2013). https://doi.org/10.1016/j.apenergy.2013.05.033

    Article  Google Scholar 

  4. Guo, W.L.: Causes and countermeasures of large wind turbine blade damage. Electr. Power Saf. Technol. 16(005), 10–13 (2014). https://doi.org/10.1049/cp.2013.2109

    Article  Google Scholar 

  5. Marsh, G.: Meeting the challenge of wind turbine blade repair. Reinf. Plast. 55, 32–36 (2011). https://doi.org/10.1016/s0034-3617(11)70112-6

    Article  Google Scholar 

  6. D.Y. Kim, H.B. Kim, W.S. Jung, S. Lim, J. Hwang, Visual testing system for the damaged area detection of wind power plant blade, in Proceedings of the 2013 44th International symposium on robotics (ISR), pp. 1–5. (2013). https://doi.org/10.1109/ISR.2013.6695675

  7. Liu, Q.X., Wang, Z.H., Long, S.G., Cai, M., Wang, X.: Research on automatic positioning system of ultrasonic testing of wind turbine blade flaws. IOP Conf. 93, 012074 (2017). https://doi.org/10.1088/1755-1315/93/1/012074

    Article  Google Scholar 

  8. Tiwari, K.A., Raisutis, R.: Refinement of defect detection in the contact and non-contact ultrasonic non-destructive testing of wind turbine blade using guided waves. Procedia Struct.l Integr. 13, 1566–1570 (2018). https://doi.org/10.1016/j.prostr.2018.12.320

    Article  Google Scholar 

  9. Shi, Y.: Phased array ultrasonic testing of glass fiber composite materials on wind turbine blades. Nondestruct. Test. (2018). https://doi.org/10.11973/wsjc201811014

    Article  Google Scholar 

  10. Neuensch, J., Furrer, R., Roemmeler, A.: Application of air-coupled ultrasonics for the characterization of polymer and polymer-matrix composite samples. Polym. Test. 56, 379–386 (2016). https://doi.org/10.1016/j.polymertesting.2016.11.002

    Article  Google Scholar 

  11. Park, B., An, Y.K., Sohn, H.: Visualization of hidden delamination and debonding in composites through noncontact laser ultrasonic scanning. Compos. Sci. Technol. 100, 10 (2014). https://doi.org/10.1016/j.compscitech.2014.05.029

    Article  Google Scholar 

  12. P. Tao, Y.Z. Zhao, K.Y. Zhou, E.T. Yao, Y. Shi, P. Xu, A research of wind turbine blade delamination detection technology based on the acoustic impact, in Proceedings of the 11th European conference on non-destructive testing: Prague, Czech Republic. (2014). https://www.ndt.net/events/ECNDT2014/app/content/Paper/640_Zhou_Rev1

  13. Rizk, P., Younes, R., Ilinca, A., Khoder, J.: Defect detection using hyperspectral imaging technology on wind turbine blade. Rem. Sens. Appl. Soc. Env. 22(3), 100522 (2021). https://doi.org/10.1016/j.rsase.2021.100522

    Article  Google Scholar 

  14. Regan, T., Beale, C.: Wind turbine blade damage detection using supervised machine learning algorithms. J. Vibr. Acoust. (2017). https://doi.org/10.1115/1.4036951

    Article  Google Scholar 

  15. Rizk, P., Saleh, N.A., Younes, R., Ilinca, A., Khoder, J.: Hyperspectral imaging applied for the detection of wind turbine blade damage and icing. Rem. Sens. Appl. Soc. Env. 18, 100291 (2020). https://doi.org/10.1016/j.rsase.2020.100291

    Article  Google Scholar 

  16. Yousuf, A., Jia, Y.J., Sokolov, P., Virk, M.S.: Study of ice accretion on wind turbine blade profiles using thermal infrared imaging. Wind Eng. (2020). https://doi.org/10.1177/0309524X20933948

    Article  Google Scholar 

  17. Zhou, B., Zhang, X., Li, H.: Study on air bubble defect evolution in wind turbine blade by infrared imaging with rheological theory. Appl. Sci. 9(22), 4742 (2019). https://doi.org/10.3390/app9224742

    Article  Google Scholar 

  18. Heuer, H., Schulze, M., Pooch, M., Gäbler, S., Nocke, A.: Review on quality assurance along the CFRP value chai non-destructive testing of fabrics, preforms and CFRP by HF radio wave techniques. Compos. B-Eng. 77, 494–501 (2015). https://doi.org/10.1016/j.compositesb.2015.03.022

    Article  Google Scholar 

  19. M.H. Schulze, H. Heuer, Textural analyses of carbon fiber materials by 2D-FFT of complex images obtained by high frequency eddy current imaging (HF-ECI), in Non-destructive characterization for composite materials, aerospace engineering, civil infrastructure, and home land security, p. 83470S. (2012). https://doi.org/10.1117/12.914832

  20. Dx, A., Pfl, B., Zpc, A.: Damage mode identification and singular signal detection of composite wind turbine blade using acoustic emission—science direct. Compos. Struct. (2020). https://doi.org/10.1016/j.compstruct.2020.112954

    Article  Google Scholar 

  21. Chen, B., Yu, S., Yu, Y., Zhou, Y.: Acoustical damage detection of wind turbine blade using the improved incremental support vector data description. Renew. Energy (2020). https://doi.org/10.1016/j.renene.2020.04.096

    Article  Google Scholar 

  22. Muñoz, G., Quiterio, C., Márquez, G., Pedro, F.: A new fault location approach for acoustic emission techniques in wind turbines. Energies 9, 40 (2016). https://doi.org/10.3390/en9010040

    Article  Google Scholar 

  23. Li, Y., Sheng, X., Lian, M.: Influence of tilt angle on eddy current displacement measurement. Nondestruct. Test. Eval. 31(4), 289–302 (2016). https://doi.org/10.1080/10589759.2015.1081905

    Article  Google Scholar 

  24. X. Sheng, Y. Li, M. Lian, Influence of coupling interference on arrayed eddy current displacement measurement. Mater. Eval. 74(12): 1675–1683 (2016). https://ndtlibrary.asnt.org/2016/InfluenceofCouplingInterferenceonArrayedEddyCurrentDisplacementMeasurement

  25. Chao, X., Li, Y., Nie, J.: Tilt angle measurement based on arrayed eddy current sensors. J. Magn. 21(4), 524–528 (2016). https://doi.org/10.4283/JMAG.2016.21.4.524

    Article  Google Scholar 

  26. Nie, J., Li, Y., She, S.: Magnetic shielding analysis for arrayed eddy current testing. J. Magn. 24(2), 328–332 (2019). https://doi.org/10.4283/JMAG.2019.24.2.328

    Article  Google Scholar 

  27. Calabrese, L., Campanella, G., Proverbio, E.: Noise removal by cluster analysis after long time ae corrosion monitoring of steel reinforcement in concrete. Constr. Build. Mater. 34, 362–371 (2012). https://doi.org/10.1016/j.conbuildmat.2012.02.046

    Article  Google Scholar 

  28. Mejia, F., Mei-Ling, S., Antonio, N.: Data quality enhancement and knowledge discovery from relevant signals in acoustic emission. Mech. Syst. Signal Process. (2015). https://doi.org/10.1016/j.ymssp.2015.02.017

    Article  Google Scholar 

  29. Kharrat, M., Ramasso, E., Placet, V., et al.: A signal processing approach for enhanced acoustic emission data analysis in high activity systems: application to organic matrix composites. Mech. Syst. Signal Process. 70–71, 1038–1055 (2016). https://doi.org/10.1016/j.ymssp.2015.08.028

    Article  Google Scholar 

  30. Li, L.: Feature extraction of ae characteristics in offshore structure model using hilbert–huang transform. Measurement (2011). https://doi.org/10.1016/j.measurement.2010.09.002

    Article  Google Scholar 

  31. Chai, M., Zhang, Z., Duan, Q.: A new qualitative acoustic emission parameter based on shannon’s entropy for damage monitoring. Mech. Syst. Signal Process. 100, 617–629 (2018). https://doi.org/10.1016/j.ymssp.2017.08.007

    Article  Google Scholar 

  32. Safaa, K.H., Jumaili, A.L., Mark, J., et al.: Characterisation of fatigue damage in composites using an acoustic emission parameter correction technique. Compos. B Eng. (2018). https://doi.org/10.1016/j.compositesb.2018.06.020

    Article  Google Scholar 

  33. Saeedifar, M., Fotouhi, M., Najafabadi, M.A., Toudeshky, H.H., Minak, G.: Prediction of quasi-static delamination onset and growth in laminated composites by acoustic emission. Compos. B Eng. (2016). https://doi.org/10.1016/j.compositesb.2015.09.037

    Article  Google Scholar 

  34. Li, Y., Yang, J.: Meta-learning baselines and database for few-shot classification in agriculture. Comput. Electron. Agric. 182(5), 106055 (2021). https://doi.org/10.1016/j.compag.2021.106055

    Article  MathSciNet  Google Scholar 

  35. Li, Y., Chao, X.: Semi-supervised few-shot learning approach for plant diseases recognition. Plant Methods 17, 1 (2021). https://doi.org/10.1186/s13007-021-00770-1

    Article  Google Scholar 

  36. Li, Y., Yang, J.: Few-shot cotton pest recognition and terminal realization. Comput. Electron. Agr. 169, 105240 (2020). https://doi.org/10.1016/j.compag.2020.105240

    Article  Google Scholar 

  37. Chao, X., Zhang, L.: Few-shot imbalanced classification based on data augmentation. Multimedia Syst. 2021, 1–9 (2021). https://doi.org/10.1007/s00530-021-00827-0

    Article  Google Scholar 

  38. Yang, Y., Zhang, Z., Mao, W., Li, Y., Lv, C.: Radar target recognition based on few-shot learning. Multimedia Syst. 2021, 1–11 (2021). https://doi.org/10.1007/s00530-021-00832-3

    Article  Google Scholar 

  39. Ming, P., Lu, J., Hu, S., Fan, X., Lin, J.: Determination of the optimal decomposition layer of wavelet de-noising based on signal band feature. Russ. J. Nondestr. Test. 55(1), 39–47 (2019). https://doi.org/10.1134/S1061830919010066

    Article  Google Scholar 

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Authors and Affiliations

  1. Beijing Goldwind Science, Beijing, China

    Xiaolei Li

  2. Goldwind Huineng Technology Co., LTD., Beijing, 100176, China

    Xiaolei Li

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  1. Xiaolei Li
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Li, X. Few-shot wind turbine blade damage early warning system based on sound signal fusion. Multimedia Systems 29, 2913–2922 (2023). https://doi.org/10.1007/s00530-021-00882-7

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