Skip to main content
SpringerLink
Log in

Deep Learning for Acoustic Pattern Recognition in Wind Turbines Aerial Inspections

  • Conference paper
  • First Online:
Proceedings of the Sixteenth International Conference on Management Science and Engineering Management – Volume 1 (ICMSEM 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 144))

  • 794 Accesses

Abstract

Wind turbine maintenance management requires new condition monitoring systems and robust algorithms for fault detection. Acoustic inspection can detect anomalies in rotatory components through the analysis of acoustic data with advanced pattern recognition algorithms. Unmanned Aerial Vehicles can carry large sensors and technologies, being one of the most relevant techniques for non-destructive inspections. This article presents a new approach to analyze the acoustic data acquired by the condition monitoring system developed in previous research studies, formed by an acoustic sensor embedded in an unmanned aerial vehicle. This approach develops different deep learning methods for acoustic signal analysis to detect abnormal patterns. One of the most suitable tools for this purpose is the Recurrent Neural Network, concretely the Long Short-Term Memory neuronal network commonly employed in sound pattern recognition. It is presented a real case study, obtaining a sound pattern classification based on the trained networks achieving 87% accuracy. This methodology aims to develop a future integrated system capable of detecting wind farms anomalies with acoustic datasets.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Acaroğlu, H., García Márquez, F.P.: Comprehensive review on electricity market price and load forecasting based on wind energy. Energies 14(22), 7473 (2021)

    Article  Google Scholar 

  2. Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp 1–6. IEEE (2017)

    Google Scholar 

  3. Bernalte Sánchez, P.J., Garcia Marquez, F.P.: New approaches on maintenance management for wind turbines based on acoustic inspection. In: Xu, J., Duca, G., Ahmed, S.E., García Márquez, F.P., Hajiyev, A. (eds.) ICMSEM 2020. AISC, vol. 1191, pp. 791–800. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-49889-4_61

    Chapter  Google Scholar 

  4. Butt, A.H., Akbar, B., et al.: Development of a linear acoustic array for aero-acoustic quantification of camber-bladed vertical axis wind Turbine. Sensors 20(20), 5954 (2020)

    Article  Google Scholar 

  5. Chacón, A.M.P., Ramírez, I.S., Márquez, F.P.G.: False alarms analysis of wind turbine bearing system. Sustainability 12(19), 7867 (2020)

    Article  Google Scholar 

  6. Choe, D.E., Kim, H.C., Kim, M.H.: Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind Turbine blades. Renew. Energy 174, 218–235 (2021)

    Article  Google Scholar 

  7. Costa, Á.M., Orosa, J.A., et al.: New tendencies in wind energy operation and maintenance. Appl. Sci. 11(4), 1386 (2021)

    Article  Google Scholar 

  8. Council, G.W.E.: Gwec| global wind report 2021. Global Wind Energy Council: Brussels, Belgium, p. 80 (2021)

    Google Scholar 

  9. Cunningham, S., Ridley, H., Weinel, J., Picking, R.: Supervised machine learning for audio emotion recognition. Pers. Ubiquit. Comput. 25(4), 637–650 (2020). https://doi.org/10.1007/s00779-020-01389-0

    Article  Google Scholar 

  10. Dao, P.B., Staszewski, W.J., et al.: Condition monitoring and fault detection in wind turbines based on cointegration analysis of scada data. Renew. Energy 116, 107–122 (2018)

    Article  Google Scholar 

  11. Delgado, I., Fahim, M.: Wind turbine data analysis and LSTM-based prediction in scada system. Energies 14(1), 125 (2021)

    Article  Google Scholar 

  12. Diez-Olivan, A., Del Ser, J., et al.: Data fusion and machine learning for industrial prognosis: trends and perspectives towards industry 4.0. Inf. Fusion 50, 92–111 (2019)

    Article  Google Scholar 

  13. Elasha, F., Shanbr, S., et al.: Prognosis of a wind turbine gearbox bearing using supervised machine learning. Sensors 19(14), 3092 (2019)

    Article  Google Scholar 

  14. Esmaieli, M., Ahmadian, M.: The effect of research and development incentive on wind power investment, a system dynamics approach. Renew. Energy 126, 765–773 (2018)

    Article  Google Scholar 

  15. Garcia Marquez, F.P., Gomez Munoz, C.Q.: A new approach for fault detection, location and diagnosis by ultrasonic testing. Energies 13(5), 1192 (2020)

    Article  Google Scholar 

  16. García Márquez, F.P., Peinado Gonzalo, A.: A comprehensive review of artificial intelligence and wind energy. Archives of Computational Methods in Engineering, pp. 1–24 (2021)

    Google Scholar 

  17. Garcia Marquez, F.P., Pliego Marugan, A., et al.: Optimal dynamic analysis of electrical/electronic components in wind turbines. Energies 10(8), 1111 (2017)

    Article  Google Scholar 

  18. García Márquez, F.P., Segovia Ramírez, I., et al.: Reliability dynamic analysis by fault trees and binary decision diagrams. Information 11(6), 324 (2020)

    Article  Google Scholar 

  19. García Márquez, F.P., Bernalte Sanchez, P.J., Segovia Ramírez, I.: Acoustic inspection system with unmanned aerial vehicles for wind turbines structure health monitoring. Structural Health Monitoring, p. 14759217211004822 (2021)

    Google Scholar 

  20. Ghoshal, A., Sundaresan, M.J., et al.: Structural health monitoring techniques for wind turbine blades. J. Wind Eng. Ind. Aerodyn. 85(3), 309–324 (2000)

    Article  Google Scholar 

  21. Glowacz, A.: Fault diagnosis of single-phase induction motor based on acoustic signals. Mech. Syst. Signal Process. 117, 65–80 (2019)

    Article  Google Scholar 

  22. Gómez Muñoz, C.Q., García Marquez, F.P., et al.: Structural health monitoring for delamination detection and location in wind turbine blades employing guided waves. Wind Energy 22(5), 698–711 (2019)

    Article  Google Scholar 

  23. Gonzalez, E., Nanos, E.M., et al.: Key performance indicators for wind farm operation and maintenance. Energy Procedia 137, 559–570 (2017)

    Article  Google Scholar 

  24. Harčarik, T., Bocko, J., Masláková, K.: Frequency analysis of acoustic signal using the fast fourier transformation in matlab. Procedia Eng. 48, 199–204 (2012)

    Article  Google Scholar 

  25. de la Hermosa González, R.R., Márquez, F.P.G., et al.: Pattern recognition by wavelet transforms using macro fibre composites transducers. Mech. Syst. Signal Process. 48(1–2), 339–350 (2014)

    Google Scholar 

  26. de la Hermosa González, R.R., Márquez, F.P.G., et al.: Maintenance management of wind turbines structures via MFCS and wavelet transforms. Renew. Sustain. Energy Rev. 48, 472–482 (2015)

    Article  Google Scholar 

  27. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)

    Article  Google Scholar 

  28. Jiménez, A.A., Zhang, L., et al.: Maintenance management based on machine learning and nonlinear features in wind turbines. Renew. Energy 146, 316–328 (2020)

    Article  Google Scholar 

  29. Khan, S., Yairi, T.: A review on the application of deep learning in system health management. Mech. Syst. Signal Process. 107, 241–265 (2018)

    Article  Google Scholar 

  30. Li, J., Chen, X., et al.: A new noise-controlled second-order enhanced stochastic resonance method with its application in wind turbine drivetrain fault diagnosis. Renew. Energy 60, 7–19 (2013)

    Article  Google Scholar 

  31. Liu, Y., Guan, L., et al.: Wind power short-term prediction based on LSTM and discrete wavelet transform. Appl. Sci. 9(6), 1108 (2019)

    Article  Google Scholar 

  32. Márquez, F., Papaelias, J., Hermosa, R.R.: Wind Turbines maintenance management based on FTA and BDD. In: International Conference on Renewable Energies and Power Quality (ICREPQ’12), pp. 4–6 (2012)

    Google Scholar 

  33. Márquez, F.P.G., Chacón, A.M.P.: A review of non-destructive testing on wind Turbines blades. Renew. Energy 161, 998–1010 (2020)

    Article  Google Scholar 

  34. Márquez, F.P.G., Karyotakis, A., Papaelias, M.: Renewable energies: Business outlook 2050. Springer (2018)

    Google Scholar 

  35. Marugán, A.P., Chacón, A.M.P., Márquez, F.P.G.: Reliability analysis of detecting false alarms that employ neural networks: a real case study on wind turbines. Reliab. Eng. Syst. Saf. 191(106), 574 (2019)

    Google Scholar 

  36. Mckinnon, C., Carroll, J., et al.: Machine learning in wind turbine o &m: Future wind and marine (2019)

    Google Scholar 

  37. Merizalde, Y., Hernández-Callejo, L., et al.: Maintenance models applied to wind turbines: a comprehensive overview. Energies 12(2), 225 (2019)

    Article  Google Scholar 

  38. Merrill, W., Weiss, G., et al.: A formal hierarchy of RNN architectures. arXiv preprint arXiv:2004.08500, p. 17 (2020)

  39. Moraleda, V.B., Marugán, A.P., Márquez, F.P.G.: Acoustic maintenance management employing unmanned aerial vehicles in renewable energies. In: Xu, J., Cooke, F.L., Gen, M., Ahmed, S.E. (eds.) ICMSEM 2018. LNMIE, pp. 969–981. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-93351-1_76

    Chapter  Google Scholar 

  40. Pak, M., Kim, S.: A review of deep learning in image recognition. In: 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), pp. 1–3. IEEE (2017)

    Google Scholar 

  41. Papaelias, M., Marquez, F.P.G., Karyotakis, A.: Non-destructive testing and condition monitoring techniques for renewable energy industrial assets. Butterworth-Heinemann (2019)

    Google Scholar 

  42. Peco Chacón, A.M., Segovia Ramírez, I., García Márquez, F.P.: State of the art of artificial intelligence applied for false alarms in wind turbines. Archives of Computational Methods in Engineering, pp. 1–25 (2021)

    Google Scholar 

  43. Pliego Marugán, A., García Márquez, F.P.: Advanced analytics for detection and diagnosis of false alarms and faults: a real case study. Wind Energy 22(11), 1622–1635 (2019)

    Google Scholar 

  44. Pliego Marugán, A., García Márquez, F.P., Lorente, J.: Decision making process via binary decision diagram. Int. J. Manage. Sci. Eng. Manage. 10(1), 3–8 (2015)

    Google Scholar 

  45. Raišutis, R., Jasiūniene, E., et al.: The review of non-destructive testing techniques suitable for inspection of the wind Turbine blades. Ultragarsas “Ultrasound” 63(2), 26–30 (2008)

    Google Scholar 

  46. Ramírez, I.S., Marugán, A.P., Márquez, F.P.G.: Remotely piloted aircraft system and engineering management: a real case study. In: Xu, J., Cooke, F.L., Gen, M., Ahmed, S.E. (eds.) ICMSEM 2018. LNMIE, pp. 1173–1185. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-93351-1_92

    Chapter  Google Scholar 

  47. Ramirez, I.S., Mohammadi-Ivatloob, B., Márqueza, F.P.G.: Alarms management by supervisory control and data acquisition system for wind Turbines. Eksploatacja i Niezawodność 23(1) (2021)

    Google Scholar 

  48. Salameh, J.P., Cauet, S., et al.: Gearbox condition monitoring in wind turbines: a review. Mech. Syst. Signal Process. 111, 251–264 (2018)

    Article  Google Scholar 

  49. Sánchez, P.J.B., Ramirez, I.S., Márquez, F.P.G.: Wind turbines acoustic inspections performed with UAV and sound frequency domain analysis. In: 2021 7th International Conference on Control, pp. 1–5. Instrumentation and Automation (ICCIA), IEEE (2021)

    Google Scholar 

  50. Segovia Ramirez, I., Das, B., Garcia Marquez, F.P.: Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles. Progress in Photovoltaics: Research and Applications, pp. 1–17 (2021)

    Google Scholar 

  51. Shafiee, M., Sørensen, J.D.: Maintenance optimization and inspection planning of wind energy assets: models, methods and strategies. Reliability Eng. Syst. Saf. 192(105), 993 (2019)

    Google Scholar 

  52. Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D 404(132), 306 (2020)

    MathSciNet  MATH  Google Scholar 

  53. Solimine, J., Niezrecki, C., Inalpolat, M.: An experimental investigation into passive acoustic damage detection for structural health monitoring of wind turbine blades. Struct. Health Monit. 19(6), 1711–1725 (2020)

    Article  Google Scholar 

  54. Staudemeyer, R.C., Morris, E.R.: Understanding lstm-a tutorial into long short-term memory recurrent neural networks. arXiv preprint arXiv:1909.09586, p. 42 (2019)

  55. Tandon, N., Choudhury, A.: A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribol. Int. 32(8), 469–480 (1999)

    Article  Google Scholar 

  56. Vamsi, I., Sabareesh, G., Penumakala, P.: Comparison of condition monitoring techniques in assessing fault severity for a wind turbine gearbox under non-stationary loading. Mech. Syst. Signal Process. 124, 1–20 (2019)

    Article  Google Scholar 

  57. Van Gerven, M., Bohte, S.: Artificial neural networks as models of neural information processing. Front. Comput. Neurosci. 11, 114 (2017)

    Article  Google Scholar 

  58. Walczak, S.: Artificial neural networks. In: Encyclopedia of Information Science and Technology, pp. 120–131. Fourth Edition, IGI Global (2018)

    Google Scholar 

  59. Willis, D., Niezrecki, C., et al.: Wind energy research: state-of-the-art and future research directions. Renew. Energy 125, 133–154 (2018)

    Article  Google Scholar 

  60. Wu, Y., Ma, X.: A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines. Renew. Energy 181, 554–566 (2022)

    Article  Google Scholar 

  61. Xiang, L., Wang, P., et al.: Fault detection of wind turbine based on scada data analysis using CNN and LSTM with attention mechanism. Measurement 175(109), 094 (2021)

    Google Scholar 

  62. Yin, A., Yan, Y., et al.: Fault diagnosis of wind turbine gearbox based on the optimized LSTM neural network with cosine loss. Sensors 20(8), 2339 (2020)

    Article  Google Scholar 

  63. Zhu, W., Liu, H., et al.: Wind turbine blade fault detection by acoustic analysis: Preliminary results. In: In: 2021 IEEE International Conference on Signal Processing, pp. 1–5. Communications and Computing (ICSPCC), IEEE (2021)

    Google Scholar 

Download references

The work reported herewith has been financially by the Direccion General de Universidades, Investigacion e Innovacion of Castilla-La Mancha, under Research Grant ProSeaWind project (Ref.: SBPLY/19/180501/000102).

Author information

Authors and Affiliations

  1. Ingenium Research Group, Universidad Castilla-La Mancha, 13071, Ciudad Real, Spain

    Pedro Jose Bernalte Sanchez, Isaac Segovia Ramirez & Fausto Pedro Garcia Marquez

Authors
  1. Pedro Jose Bernalte Sanchez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Isaac Segovia Ramirez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fausto Pedro Garcia Marquez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fausto Pedro Garcia Marquez .

Editor information

Editors and Affiliations

  1. Business School, Sichuan University, Chengdu, Sichuan, China

    Jiuping Xu

  2. Department of Industrial Engineering, Gazi University, Faculty of Engineering, Ankara, Turkey

    Fulya Altiparmak

  3. School of Mathematical Sciences, University of Khartoum, Khartoum, Sudan

    Mohamed Hag Ali Hassan

  4. University of Castilla-La Mancha (UCLM), Ciudad Real, Spain

    Fausto Pedro García Márquez

  5. Institute of Control Systems, Azerbaijan National Academy of Sciences, Baku, Azerbaijan

    Asaf Hajiyev

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

Sanchez, P.J.B., Ramirez, I.S., Marquez, F.P.G. (2022). Deep Learning for Acoustic Pattern Recognition in Wind Turbines Aerial Inspections. In: Xu, J., Altiparmak, F., Hassan, M.H.A., García Márquez, F.P., Hajiyev, A. (eds) Proceedings of the Sixteenth International Conference on Management Science and Engineering Management – Volume 1. ICMSEM 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-031-10388-9_25

Download citation

Publish with us

Policies and ethics

Search

Navigation