Full-Field Strain Prediction Applied to an Offshore Wind Turbine

  • Alexandros IliopoulosEmail author
  • Wout Weijtjens
  • Danny Van Hemelrijck
  • Christof Devriendt
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


Fatigue life is a design driver for the foundations of offshore wind turbines (OWT’s). A full-scope structural health monitoring strategy for OWT’s needs to consider the continuous monitoring of the consumption of fatigue life as an essential part. To do so, the actual stress distribution along the entire length of the structure and predominantly at the fatigue hotspots needs to be known. However installation of strain sensors at these hotspots is not always feasible since these hotspots are mainly situated beneath the water level (e.g., mudline). In practice this implies the installation of strain gauges on the monopile prior to pile driving and difficulty in maintaining these submerged sensors throughout the operational life of the turbine. Therefore, an effective and robust implemented technique using the more reliable accelerometers and very limited strain sensors at few easily accessible locations integrated within a new analytical structural dynamic approach is preferred. In this paper, a novel multi-band implementation of the well-known modal expansion approach, a.k.a. full-field strain prediction, is introduced. This technique utilizes the limited set of response data derived during a monitoring campaign and a calibrated Finite Element Model (FEM) to reconstruct the full field response of the structure. The obtained virtual responses are compared with measurements from an ongoing measurement campaign on an offshore wind turbine.


Modal expansion Full-field strain prediction Offshore wind turbines Fatigue assessment Structural health monitoring 



This research has been performed in the framework of the Offshore Wind Infrastructure Project ( and the O&O Parkwind project. The authors also acknowledge the financial support by the Agency for innovation by Science and Technology (IWT). The authors gratefully thank the people of Parkwind and Belwind and the colleagues in OWI-lab for their continuous support within this project.


  1. 1.
    Kalman, R.E., Bucy, R.S.: New results in linear filtering and prediction theory. J. Basic Eng. Trans. ASME D 83(3), 95–108 (1961)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Papadimitriou, C., Fritzen, C.-P., Kraemer, P., Ntotsios, E.: Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering. J. Struct. Control Health Monitor. 18(5), 554–73 (2011)CrossRefGoogle Scholar
  3. 3.
    Lourens, E., Papadimitriou, C., Gillijns, S., Reynders, E., De Roeck, G., Lombaert, G.: Joint-input-response estimation for structural systems based on reduced-order models and vibration data from a limited number of sensors. J. Mech. Syst. Signal Process. 29, 310–27 (2012)CrossRefGoogle Scholar
  4. 4.
    Lourens, E., Reynders, E., De Roeck, G., Degrande, G., Lombaert, G.: An augmented Kalman filter for force identification in structural dynamics. J. Mech. Syst. Signal Process. 27, 446–60 (2012)CrossRefGoogle Scholar
  5. 5.
    Maes, K., Smyth, A., De Roeck, G., Lombaert, G.: Joint input-state estimation in structural dynamics. J. Mech. Syst. Signal Process. 70–71, 445–466 (2016)Google Scholar
  6. 6.
    Maes, K., De Roeck, G., Lombaert, G., Iliopoulos, A., Van Hemelrijck, D., Devriendt, C., Guillaume, P.: Continuous strain prediction for fatigue assessment of an offshore wind turbine using Kalman filtering techniques. In: Proceedings of the Environmental, Energy and Structural Monitoring Systems (EESMS), IEEE Workshop, pp. 44–49. Trento, 9–10 July 2015. doi: 10.1109/EESMS.2015.7175850
  7. 7.
    Avitabile, P., Pingle, P.: Prediction of full field dynamic strain from limited sets of measured data. J. Shock Vib. 19, 765–85 (2012)CrossRefGoogle Scholar
  8. 8.
    Baqersad, J., Poozesh, P., Niezrecki, C., Avitabile, P.: Predicting full-field strain on a wind turbine for arbitrary excitation using displacements of optical targets measured with photogrammetry. In: Proceedings of the IMAC 33 Conference on Structural Dynamics, Orlando, FL (2015)Google Scholar
  9. 9.
    Helfrick, M.N., Niezrecki, C., Avitabile, P., Schmidt, T.: 3D digital image correlation methods for full-field vibration measurement. J. Mech. Syst. Signal Process. 25, 917–27 (2011). doi: 10.1016/j.ymssp.2010.08.013 CrossRefGoogle Scholar
  10. 10.
    Chipman, C., Avitabile, P.: Expansion of transient operating data. J. Mech. Syst. Signal Process. 31, 1–12 (2012). doi: 10.1016/j.ymssp.2012.04.013 CrossRefGoogle Scholar
  11. 11.
    Iliopoulos, A., Shirzadeh, R., Weijtjens, W., Guillaume, P., Van Hemelrijck, D., Devriendt, C.: A modal decomposition and expansion approach for prediction of dynamic responses on a monopile offshore wind turbine using a limited number of vibration sensors. J. Mech. Syst. Signal Process. 68, 84–104 (2016). doi: 10.1016/j.ymssp.2015.07.016
  12. 12.
    Maes, K., Iliopoulos, A., Weijtjens, W., Devriendt, C., Lombaert, G.: Dynamic strain estimation for fatigue assessment of an offshore monopile wind turbine using filtering and modal expansion algorithms. J. Mech. Syst. Signal Process. Available online 15 February 2016, ISSN 0888-3270, doi:
  13. 13.
    Weijtjens, W., Iliopoulos, A., Helsen, J., Devriendt, C.: Monitoring the consumed fatigue life of wind turbines on monopile foundations. In: Proceedings of the EWEA Offshore, Copenhagen (2015)Google Scholar
  14. 14.
    Weijtjens, W., Shirzadeh, R., De Sitter, G., Devriendt, C.: Classifying resonant frequencies and damping values of an offshore wind turbine on a monopile foundation. In: Proceedings of the EWEA, Barcelona (2014)Google Scholar
  15. 15.
    Heylen, W., Lammens, S., Sas, P.: Modal Analysis: Theory and Testing. Katholieke Universiteit Leuven, Leuven (1997)Google Scholar
  16. 16.
    Maia, N., Silva, J., He, J., Lieven, N., Lin, R-M., Skingle, G., To, W., Urgueira, A.: Theoretical and Experimental Modal Analysis. Research Studies Press Ltd, Somerset (1997)Google Scholar
  17. 17.
    Ewins, D.: Modal Testing 2, Theory, Practice and Application, 2nd edn. Research Studies Press Ltd, Baldock (2000)Google Scholar
  18. 18.
    Iliopoulos, A., Weijtjens, W., Van Hemelrijck, D., Devriendt, C.: Long-term prediction of dynamic responses on an offshore wind turbine using a virtual sensor approach. In: Proceedings of the 10th International Workshop on Structural Health Monitoring 2015: System Reliability for Verification and Implementation, pp. 2793–2800. Stanford, CA, 1–3 September 2015Google Scholar

Copyright information

© The Society for Experimental Mechanics, Inc. 2016

Authors and Affiliations

  • Alexandros Iliopoulos
    • 1
    Email author
  • Wout Weijtjens
    • 2
  • Danny Van Hemelrijck
    • 1
  • Christof Devriendt
    • 2
  1. 1.Department of Mechanics of Materials and ConstructionsVrije Universiteit BrusselBrusselsBelgium
  2. 2.Acoustic and Vibration Research GroupVrije Universiteit BrusselBrusselsBelgium

Personalised recommendations