Analysis of Vibration Signals of Drivetrain Failures in Wind Turbines for Condition Monitoring


In the last years, the wind industry has increased in a large scale. A wind turbine out of service leeds to high costs due to both maintenance and repair costs and the incapability of producing electricity. A substantial part of the wind turbine failures are in the drivetrain, mainly in generator and gearbox. Several recent works focuses in the study of benefits of the integration of condition monitoring with current maintenance techniques, that would drive to the reduction of costs. For condition monitoring, vibration analysis has been widely accepted as the technique that gives most information about faults in a rotating machine, thus vibration sensors are often used in wind turbine applications. In this work, data from several vibration sensors installed in 18 wind turbines in cold climate were analysed using the Wavelet Packets Transform energy. Signals were acquired for more than four years (from 2011 to 2015), registering failures in gearboxes and generators of the wind turbines. Data were obtained under varying conditions of load and speed as well as varying weather conditions. Signals were analysed with the aim of finding parameters that indicate the presence of a fault. This would be useful to predict a failure with enough time to plan a stop of the wind turbine in the proper moment for similar faults in the future.

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  1. 1.

    Villa L, Renones A, Perán J, de Miguel L (2012) Statistical fault diagnosis based on vibration analysis for gear test-bench under non-stationary conditions of speed and load. Mech Syst Signal Process 29:436–446

    Article  Google Scholar 

  2. 2.

    Zhang Z, Verma A, Kusiak A (2012) Fault analysis and condition monitoring of the wind turbine gearbox. IEEE Transactions on Energy Conversion 27(2):526–535

    Article  Google Scholar 

  3. 3.

    Sheng S, Veers P (2011) Condition monitoring. an overview. In: Mechanical failures prevention group: applied systems health management conference

  4. 4.

    Hahn B, Durstewitz M, Rohrig K (2007) Reliability of wind turbines. Springer, Berlin

    Book  Google Scholar 

  5. 5.

    García FP, Tobias AM, Pinar JM, Papaelias M (2012) Condition monitoring of wind turbines: Techniques and methods. Renew Energy 46:169–178

    Article  Google Scholar 

  6. 6.

    Artigao E, Martín-Marínez S, Honrubia-Escribano A, Gómez-Lázaro E (2018) Wind turbine reliability: a comprehensive review towards effective condition monitoring development. Appl Energy 228:1569–1583

    Article  Google Scholar 

  7. 7.

    Cempel C, Tabaszewski M (2007) Multidimensional condition monitoring of machines in non-stationary operation. Mech Syst Signal Process 21:1233–1241

    Article  Google Scholar 

  8. 8.

    Inturi V, Sabareesh G, Supradeepan K, Penumakala P (2019) Integrated condition monitoring scheme for bearing fault diagnosis of a wind turbine gearbox. J Vib Control 25(12):1852–1865

    Article  Google Scholar 

  9. 9.

    Wang T, Han Q, Chu F, Feng Z (2019) Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: a review. Mech Syst Signal Process 126:662–685

    Article  Google Scholar 

  10. 10.

    Tchakoua P, Wamkeue R, Ouhrouche M, Slaoui-Hasnaoui F (2014) Wind turbine condition monitoring: State-of-the-art review, new trends, and future challenges. Energies 7:2595–2630

    Article  Google Scholar 

  11. 11.

    Randall R, Antoni J, Chobsaard S (2000) A comparison of cyclostationary and envelope analysis in the diagnositcs of rolling element bearings. In: ICASSP’00 Proceedings of the Acoustics, Speech, and Signal Processing, pp 3882–3885

  12. 12.

    Zhang Y, Zuo H, Bai F (2013) Classification of fault location and performance degradation of a roller bearing. Measurement:, Journal of the International Measurement Confederation 46:1178–1189

    Article  Google Scholar 

  13. 13.

    Zhong X, Zhao C, Dong H, Liu X, Zeng L (2013) Rolling bearing fault diagnosis using sample entropy and 1.5 dimension spectrum based on EMD. Appl Mech Mater 278-280:1027–1031

    Article  Google Scholar 

  14. 14.

    Jiang L, Li B, Li X (2013) An imporved hht method and its application in fault diagnosis of roller bearing. Appl Mech Mater 273:264–268

    Article  Google Scholar 

  15. 15.

    Yan R, Gao R, Chen X (2014) Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process 96:1–15

    Article  Google Scholar 

  16. 16.

    Watson SJ, Xiang BJ, Yang W, Tavner P, Crabtree CJ (2010) Condition monitoring of the power output of wind turbine generators using wavelets. IEEE Transactions on Energy Conversion 25 (3):715–721

    Article  Google Scholar 

  17. 17.

    Teng E, Wang W, Ma H, Liu Y (2019) Adaptive fault detection of the bearing in wind turbine generators using parameterless empirical wavelet transform and margin factor. J Vib Control 25(6):1263–1278

    Article  Google Scholar 

  18. 18.

    Gómez M, Castejón C, García-Prada J (2014) Incipient Fault Detection in Bearings through the use of WPT energy and Neural Networks. Advances in Condition Monitoring of Machinery in Non-Stationary Operations. Lecture Notes in Mechanical Engineering, Springer

  19. 19.

    Gómez M, Castejón C, García-Prada J (2015) New stopping criteria for crack detection during fatigue tests of failway axles. Eng Fail Anal 56:530–537

    Article  Google Scholar 

  20. 20.

    Gómez M, Castejón C, García-Prad J (2016) Crack detection in rotating shafts based on the 3x energy. analytical and experimental analyses. Mech Mach Theory 96:94–106

    Article  Google Scholar 

  21. 21.

    Gómez MJ, Castejón C, Corral E, García-Prada JC (2016) Analysis of the influence of crack location for diagnosis in rotating shafts based on 3 x energy. Mechanism and Machine Theory 103:167–173

    Article  Google Scholar 

  22. 22.

    Gómez MJ, Castejón C, García-Prada JC (2016) Automatic condition monitoring system for crack detection in rotating machinery. Reliab Eng Syst Saf 152:239–247

    Article  Google Scholar 

  23. 23.

    Gómez MJ, Castejón C, García-Prada JC (2016) Review of advances in the application of the wavelet transform to cracked rotors diagnosis. Algorithms 9:19–32

    Article  Google Scholar 

  24. 24.

    Yen G, Lin K (2000) Wavelet packet feature extraction for vibration monitoring. IEEE Trans Ind Electron 47(3):650–667

    Article  Google Scholar 

  25. 25.

    Moreno R, Pintado P, Chicharro J, Morales A, Nieto A (2009) Methodology for evaluating neural networks input for gear fault detection. In: IEEE international conference on mechatronics, Malaga, Spain, pp 1–6

  26. 26.

    Mallat S (1998) A wavelet tour of signal processing. Academic Press

  27. 27.

    Rai V, Mohanty A (2007) Bearing fault diagnosis using fft of intrinsic mode functions in hilbert-huand transform. Mech Syst Signal Process 21:2607–2615

    Article  Google Scholar 

  28. 28.

    Saruhan H, Sandemir S, Çiçek A, Uygur I (2014) Vibration analysis of rolling element bearings defects. J Appl Res Technol 12:384–395

    Article  Google Scholar 

  29. 29.

    Hizarci B, Ümütlü R, Ozturk H, Kiral Z (2019) Vibration region analysis for condition monitoring of gearboxes using image processing and neural networks. Experimental Techniques.

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The authors wish to thank to the University Carlos III of Madrid for funding under the grant for mobility of researchers.

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Correspondence to M. J. Gómez.

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Gómez, M.J., Marklund, P., Strombergsson, D. et al. Analysis of Vibration Signals of Drivetrain Failures in Wind Turbines for Condition Monitoring. Exp Tech 45, 1–12 (2021).

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  • Vibration signals
  • Wind turbines
  • Faults detection
  • Condition monitoring