Print Email Facebook Twitter A comparison of two Kalman-type filters for robust extrapolation of offshore wind turbine support structure response Title A comparison of two Kalman-type filters for robust extrapolation of offshore wind turbine support structure response Author Tatsis, K. Lourens, E. (TU Delft Offshore Engineering) Contributor Bakker, Jaap (editor) Frangopol, Dan M. (editor) van Breugel, Klaas (editor) Date 2016 Abstract Quasi-periodic loading resulting from waves and a rotationally sampled wind field often leads to fatiguedriven designs for offshore wind turbine support structures. The uncertainty on wind and wave loading, together with large modelling uncertainties, lead to large discrepancies between the observed and predicted dynamic behaviour of these structures. Among many recent-developed techniques for monitoring of true fatigue damage development, two promising Kalman-type filters are compared, namely the recently proposed Dual Kalman filter (DKF) and the Gillijns and De Moor filter (GDF). The filters are applied to synthetic vibration data in order to predict the global response of a lattice support structure assuming large modelling uncertainties and no knowledge of the input forces. A critical assessment of both filterswith regard to requirements on the available data and tuning of the filter parameters is presented. To reference this document use: http://resolver.tudelft.nl/uuid:3753b539-f2c2-4bbb-aa73-fca0c75745b6 DOI https://doi.org/10.1201/9781315375175-25 Publisher Taylor and Francis ISBN 978-1-138-02847-0 Source Life-Cycle of Engineering Systems: Emphasis on Sustainable Civil Infrastructure: Proceedings of the 5th International Symposium on Life-Cycle Engineering, Delft, Netherlands Event 5th International Symposium on Life-Cycle Engineering, 2016-10-16 → 2016-10-20, Delft, Netherlands Series Life-Cycle of Civil Engineering Systems Part of collection Institutional Repository Document type conference paper Rights © 2016 K. Tatsis, E. Lourens Files PDF 1.pdf 1.11 MB Close viewer /islandora/object/uuid:3753b539-f2c2-4bbb-aa73-fca0c75745b6/datastream/OBJ/view