10.18710/LSVFOL Sakaris, Christos Christos Sakaris https://orcid.org/0000-0002-7211-4520 https://ror.org/02gagpf75 Schlanbusch, Rune Rune Schlanbusch https://orcid.org/0000-0002-0730-845X https://ror.org/02gagpf75 Lee, Chern Fong Chern Fong Lee https://orcid.org/0000-0001-7397-6002 https://ror.org/02qte9q33 Ong, Muk Chen Muk Chen Ong https://orcid.org/0000-0001-5288-5857 https://ror.org/02qte9q33 Tutkun, Murat Murat Tutkun https://orcid.org/0000-0003-0702-5051 https://ror.org/02jqtg033 Nygaard, Tor Anders Tor Anders Nygaard https://orcid.org/0000-0001-6803-3763 https://ror.org/02jqtg033 DataverseNO 2026 Engineering Mooring lines Chain Synthetic Acceleration Damage AIMWind Schlanbusch, Rune Rune Schlanbusch NORCE Research AS DataverseNO NORCE Research AS Sakaris, Christos Christos Sakaris NORCE Research AS NORCE Research AS Institute for Energy (IFE) University of Stavanger 2026-02-12 2026-02-19 2023-03-31/2023-03-31 2023-04-01/2023-04-01 2023-03-31/2023-03-31 2023-04-01/2023-04-01 Simulated acceleration data /article/10.1088/1742-6596/2875/1/012032 10.3390/S24020543 10.1109/CDC49753.2023.10383638 979-8-3313-0812-4 10.18710/LSVFOL/UZ3E41 10.18710/LSVFOL/XODCAQ 10.18710/LSVFOL/ZVEKRY 10.18710/LSVFOL/MRS2ZF 10.18710/LSVFOL/XREJQV 4160597058 2297630659 561857 505289 5505 application/zip application/zip application/pdf application/pdf text/plain 1.0 Creative Commons CC0 1.0 Universal Public Domain Dedication. About 65 GW of onshore wind turbine installations in Europe will reach end-of-design-life by 2028. It is time for the operators to decide on one of the three end-of-life scenarios, namely, decommissioning, lifetime extension, or repowering. The last two options will increase the operating life and thus reduce lifecycle costs. These end-of-life decisions require careful consideration of the accumulated fatigue life of each turbine in a wind farm to minimize monetary risk for the wind farm operators. Today, this decision is primarily based on a single point assessment by the certification authority. AIMWind (Analytics for asset Integrity Management of Windfarms) project (https://www.aimwind.no/) proposes a continuous evaluation of wind farm health based on big data analytics using multimodal data such as wind, operational data, weather, condition monitoring, and inspection logs across a wind farm. Conventional approaches to fatigue estimation are slow and inadequate to achieve these goals, especially in large wind farms. Such a continuous health assessment will facilitate not only accurate life predictions but also continuous improvement of wind turbine operations to ensure long life and high availability. In the context of the AIMWind project, simulated acceleration data representing the healthy and damaged chain or synthetic mooring lines of Floating Offshore Wind Turbines have been generated. The data have been used for the validation of various machine learning methods used for damage detection in the considered chain and synthetic mooring lines. 3DFloat SIMA Stavanger Kjeller Research Council of Norway Grant number: 312486