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| one publication added to basket [323107] |
Prediction of remaining fatigue life of welded joints in wind turbine support structures considering strain measurement and a joint distribution of oceanographic data
Maia, Q.A.; Weijtjens, W.; Devriendt, C.; Morato, P.G.; Rigo, P.; Sørensen, J.D. (2019). Prediction of remaining fatigue life of welded joints in wind turbine support structures considering strain measurement and a joint distribution of oceanographic data. Mar. Struct. 66: 307-322. https://dx.doi.org/10.1016/j.marstruc.2019.05.002
In: Marine Structures. Elsevier: Oxford. ISSN 0951-8339; e-ISSN 1873-4170, meer
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| Author keywords |
Fatigue; Remaining life; Reliability; Wind turbines; Measurement; Weldedjoints; Support structures; Offshore; Inspection; Monitoring; SCADA;Measured strain |
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- Morato, P.G., meer
- Rigo, P., meer
- Sørensen, J.D.
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| Abstract |
Reassessing the remaining fatigue life of the wind turbine support structures becomes more and more crucial for operation, maintenance, and life extension when they are reaching the end of their design service life. By using measured oceanographic and strain data, each year, remaining fatigue life can be updated to adapt the operation to real loading conditions. Previous works have not put attention to address the complexity of offshore loading combinations and as-constructed state of the structure in estimating structural responses for fatigue behaviour to stochastically predict the remaining fatigue life. The present paper links the oceanographic data to fatigue damage by using measured strain, and uses the Bayesian approach to update the joint distribution of the oceanographic data. Consequently, the failure probability of the support structure can be updated and so the predicted fatigue life. The year-to-year variation of the 10-min mean wind speed, the unrepresentativeness of measured strain, the measurement uncertainty, and corrosion are considered together with uncertainties in Miner's rule and S-N curves. The present research shows that the real oceanographic data can be used to adjust the predicted remaining fatigue life and eventually give decision support for the wind turbine operation. |
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