Prognostics and Health Management of Wind Turbines—Current Status and Future Opportunities

  • Shuangwen Sheng


The global wind industry has seen tremendous growth during the past two decades. However, the industry is challenged by premature component failures, which lead to increased turbine downtime and subsequently, cost of energy for wind power. To mitigate the impacts from these failures, the wind industry has been exploring various areas for improvements ranging from product design, new materials or lubricants, to operation and maintenance (O&M) practices. Condition-based maintenance or prognostics and health management (PHM) has been explored as one enabling technology for improving O&M practices. This chapter provides a brief overview of wind turbine PHM with a focus on operational data mining and condition monitoring of drivetrains. Some future research and development opportunities in wind turbine PHM are also briefly discussed.


PHM Wind turbine Diagnostics Prognostics Operation and maintenance 



This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-08GO28308 with the National Renewable Energy Laboratory. Funding for the work was provided by the DOE Office of Energy Efficiency and Renewable Energy, Wind and Water Power Technologies Office. The author would also like to acknowledge the NREL condition monitoring and O&M research partners for their support.

The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or to allow others to do so, for U.S. Government purposes.


  1. 1.
    Global Energy Research Council, Global Wind Report, 2015Google Scholar
  2. 2.
    P. Tavner, Offshore Wind Turbine Reliability (Supergen Wind Training, Manchester, UK, 2011)Google Scholar
  3. 3.
    International Renewable Energy Agency, Renewable Energy Technologies: Cost Analysis Series, Volume 1: Power Sector, Wind Power, May, 2012Google Scholar
  4. 4.
    Y. Guo, J. Keller, W. LaCava, Planetary gear load sharing of wind turbine drivetrains subjected to non-torque loads. Wind Energy 18(4), 757–768 (2015)CrossRefGoogle Scholar
  5. 5.
    Y. Guo, R. Bergua, J. van Dam, J. Jove, J. Campbell, Improved wind turbine drivetrain designs to minimize the impacts of non-torque loads. Wind Energy 18(12), 2199–2222 (2015)CrossRefGoogle Scholar
  6. 6.
    A. Erdemir, A. Greco, J. Keller, S. Sheng, Material wear and fatigue in wind turbine systems. Wear 302, 1583–1591 (2013)CrossRefGoogle Scholar
  7. 7.
    A. Greco, K. Mistry, V. Sista, O. Eryilmaz, A. Erdemir, Friction and wear behavior of boron based surface treatment and nano-particle lubricant additives for wind turbine gearbox applications. Wear 271, 1754–1760 (2011)CrossRefGoogle Scholar
  8. 8.
    S. Sheng, Monitoring of wind turbine gearbox condition through oil and wear debris analysis: A full-scale testing perspective. Tribol. Trans. 59(1), 149–162 (2016)CrossRefGoogle Scholar
  9. 9.
    E. Byon, L. Ntaimo, Y. Ding, Optimal maintenance strategies for wind turbine systems under stochastic weather conditions. IEEE Trans. Reliab. 59(2), 393–404 (2010)CrossRefGoogle Scholar
  10. 10.
    Z.S. Chen, Y.M. Yang, H. Zheng, A technical framework and roadmap of embedded diagnostics and prognostics for complex mechanical systems in prognostics and health management systems. IEEE Trans. Reliab. 61(2), 314–322 (2012)CrossRefGoogle Scholar
  11. 11.
    M. Jouin, R. Gouriveau, D. Hissel, M.C. Péra, N. Zerhouni, Prognostics and health management of PEMFC—State of the art and remaining challenges. Int. J. Hydrog. Energy 38(35), 15307–15317 (2013)CrossRefGoogle Scholar
  12. 12.
    J. Coble, R. Ramuhalli, L. Bond, J.W. Hines, B. Upadhyaya, A review of prognostics and health management applications in nuclear power plants. Int. J. Progn. Heal. Manag. 6 (2015)Google Scholar
  13. 13.
    P. Joschko, A.H. Widok, S. Appel, S. Greiner, H. Albers, B. Page, Modeling and simulation of offshore wind farm O&M processes. Environ. Impact Assess. Rev. 52, 31–39 (2015)CrossRefGoogle Scholar
  14. 14.
    M. Scheu, D. Matha, M. Hofmann, M. Muskulus, Maintenance strategies for large offshore wind farms. Energy Procedia 24, 281–288 (2012)CrossRefGoogle Scholar
  15. 15.
    C. Perera, C.H. Liu, S. Jayawardena, M. Chen, A survey on Internet of Things from industrial market perspective. IEEE Access. 2, 1660–1679 (2014)CrossRefGoogle Scholar
  16. 16.
    K.S. Wang, V.S. Sharma, Z.Y. Zhang, SCADA data based condition monitoring of wind turbines. Adv. Manuf. 2, 61–69 (2014)CrossRefGoogle Scholar
  17. 17.
    C. Kaidis, B. Uzunoglu, F. Amoiralis, Wind turbine reliability estimation for different assemblies and failure severity categories. IET Renew. Power Gener. 9(8), 892–899 (2015)CrossRefGoogle Scholar
  18. 18.
    F. Castellani, A. Garinei, L. Terzi, D. Astolfi, M. Moretti, A. Lombardi, A new data mining approach for power performance verification of an onshore wind farm. Diagnostyka 14(4), 35–42 (2013)Google Scholar
  19. 19.
    S. Butler, J. Ringwood, F. O’Connor, Exploiting SCADA system data for wind turbine performance monitoring, in Proceedings of the Conference on Control and Fault-Tolerant Systems, Nice, France, 2013Google Scholar
  20. 20.
    N. Yampikulsakul, E. Byon, S. Huang, S. Sheng, M. Yu, Condition monitoring of wind power system with nonparametric regression analysis. IEEE Trans. Energy Convers. 29(2), 288–299 (2014)CrossRefGoogle Scholar
  21. 21.
    W. Yang, P.J. Tavner, C.J. Crabtree, Y. Feng, Y. Qiu, Wind turbine condition monitoring: technical and commercial challenges. Wind Energy 17(5), 673–693 (2014)CrossRefGoogle Scholar
  22. 22.
    T.W. Verbruggen, Wind Turbine Operation and Maintenance Based on Condition Monitoring, Energy Research Center of the Netherlands, Petten, The Netherlands, 2003Google Scholar
  23. 23.
    X.S. Si, W. Wang, C.H. Hu, D.H. Zhou, Remaining useful life estimation—a review on the statistical data driven approaches. Eur. J. Oper. Res. 213(1), 1–14 (2011)MathSciNetCrossRefGoogle Scholar
  24. 24.
    K. Medjaher, D.A. Tobon-Mejia, N. Zerhouni, Remaining useful life estimation of critical components with application to bearings. IEEE Trans. Reliab. 61(2), 292–302 (2012)CrossRefGoogle Scholar
  25. 25.
    P. Baraldi, M. Compare, S. Sauco, E. Zio, Ensemble neural network-based particle filtering for prognostics. Mech. Syst. Signal Process. 41, 288–300 (2013)CrossRefGoogle Scholar
  26. 26.
    F. Zhao, Z. Tian, Y. Zeng, Uncertainty quantification in gear remaining useful life prediction through an integrated prognostics method. IEEE Trans. Reliab. 62(1), 146–159 (2013)CrossRefGoogle Scholar
  27. 27.
    S. Sheng, P. Veers, Wind turbine drivetrain condition monitoring—an overview, in Proceedings of the Mechanical Failures Prevention Group: Applied Systems Health Management Conference, Virginia Beach, Virginia, 2011Google Scholar
  28. 28.
    Y. Lu, J. Tang, H. Luo, Wind turbine gearbox fault detection using multiple sensors with features level data fusion. J. Eng. Gas Turbines Power 134.4, 042501-1–8 (2012)Google Scholar
  29. 29.
    R. Dupuis, Application of oil debris monitoring for wind turbine gearbox prognostics and health management, in Proceedings of the Prognostics and Health Management Society Annual Conference, Portland, Oregon, 2010Google Scholar
  30. 30.
    S. Sheng, Investigation of various condition monitoring techniques based on a damaged wind turbine gearbox, in Proceedings of the 8th International Workshop on Structural Health Monitoring, Stanford, California, 2011Google Scholar
  31. 31.
    D. Coronado, K. Fischer, Condition monitoring of wind turbines: state of the art, user experience and recommendations, in Fraunhofer Institute for Wind Energy and Energy System Technology IWES Northwest, Bremerhaven, Germany, 2015Google Scholar
  32. 32.
    S. Sheng, Prognostics and health management of wind turbines: Current status and future opportunities, presented at the Probabilistic Prognostics and Health Management of Energy Systems Workshop, IIha Solteira, Brazil, 14–15 Dec 2015Google Scholar
  33. 33.
    S. Sheng, Improving component reliability through performance and condition monitoring data analysis, presented at Wind Farm Data Management & Analysis North America, Houston, Texas, 25–26 Mar 2015Google Scholar
  34. 34.
    Y. Dalgic, I. Lazakis, I. Dinwoodie, D. McMillan, M. Revie, Advanced logistics planning for offshore wind farm operation and maintenance activities. Ocean Eng. 101, 211–226 (2015)CrossRefGoogle Scholar
  35. 35.
    J.J. Nielsen, J.D. Sørensen, On risk-based operation and maintenance of offshore wind turbine components. Reliab. Eng. Syst. Saf. 96(1), 218–229 (2011)CrossRefGoogle Scholar
  36. 36.
    S. Sankararaman, K. Goebel, Uncertainty in prognostics and systems health management. Int. J. Progn. Heal. Manag. 6 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.National Renewable Energy LaboratoryGoldenUSA

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