Estimating Fatigue in the Main Bearings of Wind Turbines Using Experimental Data

  • Giovanni M. FavaEmail author
  • Sauro Liberatore
  • Babak Moaveni
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


Axial bearing failures, in modern wind turbines, have been one of the major sources of unplanned maintenance expenses for the wind energy business. Although the causes of such failures are unclear, its statistics do increase in wind farms where the wind conditions range in considerably larger spans. It is common belief that torsional vibrations, combined with axial ones resulting from these extreme wind conditions are the major causes of high cycle, fatigue affecting the bearing. In this study, the strain measurements on blades are used to estimate the internal force time histories at the location of the main bearing. A rigid body equilibrium analysis was performed using moments and forces measured at the blades to estimate the force and moments acting on the low speed shaft. Experimental wind data was used to compare the difference between strong wind speed events and low wind speed events. The approach uses the blade bending-moments to isolate the axial excitations and is validated by measured data from an instrumented turbine with four interferometric strain sensors and four temperature sensors for each blade. The instrumented turbine is part of an onshore wind farm site, in Cohocton, NY, where wind conditions span in a large range and turbines are often placed in stop conditions. Preliminary data has shown some large excitations that could eventually lead to crack initiation.


Wind turbines Stress estimation Fatigue analysis Main bearing monitoring 



The opinions, findings, and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the organizations involved in this project.


  1. 1.
    Wasilczuk, M., Gawarkiewicz, R., Bastian, B.: Analysis of failures of high speed shaft bearing system in a wind turbine. In: 9th International Conference on Tribology, pp. 3–4 (2018)Google Scholar
  2. 2.
    May, A., McMillan, D., Thons, S.: Economic analysis of condition monitoring systems for offshore wind turbine sub-systems, semantic scholar (2014)Google Scholar
  3. 3.
    Shakya, P., Darpe, A., Kulkarni, M.: Vibration based fault diagnosis in rolling element bearings: ranking of various time, frequency and time-frequency domain data based damage identification. Int. J. Cond. Monit. 3, 1–10 (2013)Google Scholar
  4. 4.
    Mollasalehi, E., Wood, D., Sun, Q.: Indicative fault diagnosis of wind turbine generator bearings using tower sound and vibration: In: 24th International Congress on Sound and Vibration (2017)Google Scholar
  5. 5.
    Schultz, R., Verstockt, S., Vermeiren, J., Loccufier, M., Stockman, K., Van Hoecke, S.: Thermal imaging for monitoring rolling element bearings. In: Proceedings of the 12th International Conference on Quantitative InfraRed Thermography (2014)Google Scholar
  6. 6.
    Yang, W., Tavner, P., Crabtree, C., Feng, Y., Qiu, Y.: Wind turbine condition monitoring: technical and commercial challenges. Wind Energy 17(5) (2012)Google Scholar
  7. 7.
    Iftimie, N., et al.: Wireless sensors for wind turbine blades monitoring. IOP Conference, vol. 209 (2017)Google Scholar
  8. 8.
    Sonawane, P., Kharate, N.K.: Fault diagnosis of windmill by FFT analyzer. Int. J. Innov. Eng. Technol. 4(4), 47–54 (2014)Google Scholar
  9. 9.
    Greco, A., et al.: Bearing reliability—White Etching Cracks (WEC), Argonne National Laboratory, Energy Systems Division, NREL Gearbox Reliability Collaborative (2013)Google Scholar
  10. 10.
    Yagi, S.: Bearings for wind turbine. NTN Technical Review, no. 71 (2004)Google Scholar
  11. 11.
    SAP 2000 18, Computers and Structures, Inc., Berkeley, CA (2018)Google Scholar
  12. 12.
    Showers, D.: System Identification for the Clipper Liberty C96 Wind Turbine, Ph.D. Dissertation. University of Minnesota, Minneapolis (2014)Google Scholar
  13. 13.
    Clipper Windpower Plc, Liberty 2.5 MW Wind Turbine: Facts and Specifications (2009)Google Scholar
  14. 14.
    Hartman, D., Greenwood, M., Miller, D.: High Strength Glass Fibers, AGY, p. 3 (1996)Google Scholar
  15. 15.
    Neptull, Hesse, Germany (2012)Google Scholar
  16. 16.
    Kovacic, B., Kamnik, R., Štrukelj, A., Vatin, N.: Processing of signals produced by strain gauges in testing measurements of the bridges. Procedia Eng. 117, 795–801 (2015)CrossRefGoogle Scholar
  17. 17.
    MATLAB R2017a, MathWorks Inc., Natick, MA (2018)Google Scholar

Copyright information

© Society for Experimental Mechanics, Inc. 2020

Authors and Affiliations

  • Giovanni M. Fava
    • 1
    Email author
  • Sauro Liberatore
    • 1
  • Babak Moaveni
    • 2
  1. 1.Department of Mechanical EngineeringTufts UniversityNewtonUSA
  2. 2.Department of Civil and Environmental EngineeringSchool of Engineering, Tufts UniversityMedfordUSA

Personalised recommendations