Skip to main content

Advertisement

SpringerLink
Log in

Vibration monitoring, fault detection, and bearings replacement of a real wind turbine

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

Wind turbines are growing rapidly in size and diameter. Nowadays, most wind turbines being installed are around 100 m in height and 80–120 m in diameter. Another important characteristic of wind farms is that they are usually far from urban centers. These peculiarities play an important role when analyzing the operation and maintenance costs and its impact in the wind farm project. In remote centers, it becomes crucial to predict and prevent unnecessary maintenance breakdowns and costs. An efficient solution to prevent faults on wind turbines is through condition monitoring. Faults could be prevented by analyzing data from sensors placed around the wind turbines to measure mainly oil quality, temperature, and vibration. In this paper, accelerometers were placed on the main components of a real wind turbine and a vibration-based condition monitoring methodology was applied using signal processing techniques such as Fourier transform, and envelope analysis with Hilbert transform. A bearing fault was discovered and the vibration characteristics were analyzed before and after the bearing replacement.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31

Similar content being viewed by others

References

  1. Yang J, Yang P (2016) Random vibration and dynamic analysis of a planetary gear train in a wind turbine. Shock Vib. doi:10.1155/2016/6292953

    Google Scholar 

  2. Sheng S and Veers P (2011) Wind turbine drivetrain condition monitoring—an overview. In: Mechanical Failures Prevention Group, applied systems health management conference; 2011 May 10–12; Virginia Beach, Virginia. NREL, USA

  3. Global Wind Energy Council (2016a) GWEC The international trade association for the wind power industry. http://www.gwec.net/global-figures/wind-in-numbers/. Accessed 27 Mar 2016

  4. Global Wind Energy Council (2016b) GWEC The international trade association for the wind power industry. http://www.gwec.net/brazil-windpower-2016/. Accessed 27 Apr 2016

  5. Azevedo HDM, Araújo AM, Bouchonneau N (2016) A review of wind turbine bearing condition monitoring: state of the art and challenges. Renew Sustain Energy Rev 56:368–379. doi:10.1016/j.rser.2015.11.032

    Article  Google Scholar 

  6. Márquez FP, Tobias AM, Pérez JM, Papaelias M (2012) Condition monitoring of wind turbines: techniques and methods. Renew Energy 46:169–178. doi:10.1016/j.renene.2012.03.003

    Article  Google Scholar 

  7. Koulocheris D, Gyparakis G, Stathis A, Costopoulos T (2013) _ Vibration signals and condition monitoring for wind turbines. Engineering 5:948–955. doi:10.4236/eng.2013.512116

    Article  Google Scholar 

  8. Zimroz R, Bartekmus W, Barszcz T, Urbanek J (2014) Diagnostics of bearings in presence of strong operating conditions non-stationarity-a procedure of load-dependent features processing with application to wind turbine bearings. Mech Syst Signal Process 46(1):16–27. doi:10.1016/j.ymssp.2013.09.010

    Article  Google Scholar 

  9. Romero A, Lage Y, Soua S, Wang B, Gan T-H (2016) Vestas V90-3 MW wind turbine gearbox health assessment using a vibration-based condition monitoring system. Shock Vib. doi:10.1155/2016/6423587

    Google Scholar 

  10. Nie M, Wang L (2013) Review of condition monitoring and fault diagnosis technologies for wind turbine gearbox. In: 2nd International Through-life Engineering Services Conference; 2013 November 5–6; Cranfield, Cranfield University, UK. Elsevier, UK, pp 287–290. doi:10.1016/j.procir.2013.07.018

  11. He D, Bechhoefe E, Saxena A (2013) Editorial: special issue on wind turbine prognostics and health management. Int J Health Proagnostics 4(2):1–2

  12. Tachakoua P, Wamkeue R, Ouhrouche M et al (2014) Wind turbine condition monitoring: state-of-the-art review, new trends, and future challenges. Energies 7(4):2595–2630. doi:10.3390/en7042595

    Article  Google Scholar 

  13. Sheng S, Keller J and Glinsky C (2013) Gearbox reliability collaborative update. Sandia Reliability Workshop; 2013 August 13–14; Albuquerque, NM. NREL, USA

  14. Xueli A, Dongxiang J, Jie C, Chao L (2011) Application of the intrinsic time-scale decomposition method to fault diagnosis of wind turbine bearing. J Vib Control 18(2):240–245. doi:10.1177/1077546311403185

    Google Scholar 

  15. Lin B, Chang P (2016) Fault diagnosis of rolling element bearing using more robust spectral kurtosis and intrinsic time-scale decomposition. J Vib Control 22(12):2921–2937. doi:10.1177/1077546314547727

    Article  Google Scholar 

  16. Sun P, Li J, Wang C, Lei X (2016) A generalized model for wind turbine anomaly identification based on SCADA data. Appl Energy 168:550–567. doi:10.1016/j.apenergy.2016.01.133

    Article  Google Scholar 

  17. Astolfi D, Castellani F, Garinei A, Terzi L (2015) Data mining techniques for performance analysis of onshore wind farms. Appl Energy 148:220–233. doi:10.1016/j.apenergy.2015.03.075

    Article  Google Scholar 

  18. Sheng S (2013) Report on wind turbine subsystem reliability—a survey of various database. Report. National Renewable Energy Laboratory (USA)

  19. Whittle M (2015) Wind turbine generator reliability: an exploration of the root causes of generator bearing failures. Doctoral dissertation. Durham University, Durham (NC)

    Google Scholar 

  20. VDI 3834 (2009) Measurement and evaluation of the mechanical vibration of wind energy turbines and their components—Part 1—Onshore wind energy turbines with gears. International Standard

  21. SKF product information high frequency accelerometer CMSS 2100F. SKF Reliability Systems. http://www.skf.com/group/system/SearchResult.html?search=CMSS+2100F. Accessed 12 Aug 2014

  22. SKF product information low frequency accelerometer CMSS 2200. SKF Reliability. http://www.skf.com/group/system/SearchResult.html?search=CMSS+2200. Accessed 12 Aug 2014

  23. SKF product information datalogger IMx-W. SKF Reliability. http://www.skf.com/group/system/SearchResult.html?search=IMx-W. Accessed 12 Aug 2014

  24. Hirschmann (2013) Product information switch RS2-4TX/1FX EEC. https://www.e-catalog.beldensolutions.com/link/57078-24455-49814-49855-351756-34790/en/conf/. Accessed 12 Aug 2014

  25. SKF product information CMCP240. SKF Condition Monitoring Custom Products. http://www.stiweb.com/v/vspfiles/downloadables/SKF%20Data%20Sheets/cmcp240.pdf. Accessed 12 Aug 2014

  26. Azevedo HDM (2015) Um método para identificação de falhas em componentes e subcomponentes de turbinas eólicas através de monitoramento de condição baseado em vibração. Master dissertation. Federal University of Pernambuco, Recife (PE)

    Google Scholar 

  27. Wowk V (1991) Machinery vibration measurement and analysis, 1st edn. McGraw-Hill, USA

    Google Scholar 

  28. Miao Q, Cong L, Pecht M (2011) Identification of multiple characteristic components with high accuracy and revolution using the zoom interpolated discrete Fourier transform. Meas Sci Technol. doi:10.1088/0957-0233/22/5/055701

    Google Scholar 

  29. Jayaswal P, Agrawal B (2011) New trends in wind turbine condition monitoring system. Int J Emerg Trends Eng Dev 3(1):133–148

    Google Scholar 

  30. Miller AJ (1999) A new wavelet basis for the decomposition of gear motion error signals and its application to gearbox diagnostics. Doctoral dissertation. The Pennsylvania State University, Pennsylvania (USA)

  31. Cheng J, Yang Y, Yu D (2010) The envelope order spectrum based on generalized demodulation time–frequency analysis and its application to gear fault diagnosis. Mech Syst Signal Process 24(2):508–521. doi:10.1016/j.ymssp.2009.07.003

    Article  Google Scholar 

  32. Tandon N, Nakra BC (1992) Comparison of vibration and acoustic measurement techniques for the condition monitoring of rolling element bearings. Tribol Int 25(3):205–512. doi:10.1016/0301-679X(92)90050-W

    Article  Google Scholar 

  33. Mcfadden PD (1986) Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration. J Vib Acoust Stress Reliab Des 108(2):165–170. doi:10.1115/1.3269317

    Article  Google Scholar 

  34. Machinery Lubrication Magazine (2015) One vibration analysis expert shares his views about the importance of oil analysis. http://www.machinerylubrication.com/Read/36/oil-analysis-vibes. Accessed 01 Sep 2016

Download references

Acknowledgements

This work was partly funded by Brazilian research councils Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—CAPES, Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq and Pró-Reitoria de Pesquisa e Pós-Graduação—UFPE/PROPESQ.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering/Fluid Laboratory, Federal University of Pernambuco, Recife, Brazil

    Henrique D. M. de Azevedo, Pedro H. C. de Arruda Filho, Alex M. Araújo, Nadège Bouchonneau & Janardan S. Rohatgi

  2. Department of Electronics and Systems, Federal University of Pernambuco, Recife, Brazil

    Ricardo M. C. de Souza

Authors
  1. Henrique D. M. de Azevedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pedro H. C. de Arruda Filho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alex M. Araújo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nadège Bouchonneau
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Janardan S. Rohatgi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ricardo M. C. de Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alex M. Araújo.

Additional information

Technical Editor: Kátia Lucchesi Cavalca Dedini.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Azevedo, H.D.M., de Arruda Filho, P.H.C., Araújo, A.M. et al. Vibration monitoring, fault detection, and bearings replacement of a real wind turbine. J Braz. Soc. Mech. Sci. Eng. 39, 3837–3848 (2017). https://doi.org/10.1007/s40430-017-0853-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40430-017-0853-2

Keywords

Search

Navigation