Replication Data for: Damage detection in chain and synthetic mooring lines of Floating Offshore Wind Turbinesdoi:10.18710/LSVFOLDataverseNO2026-02-191Sakaris, Christos; Schlanbusch, Rune; Lee, Chern Fong; Ong, Muk Chen; Tutkun, Murat; Nygaard, Tor Anders, 2026, "Replication Data for: Damage detection in chain and synthetic mooring lines of Floating Offshore Wind Turbines", https://doi.org/10.18710/LSVFOL, DataverseNO, V1Replication Data for: Damage detection in chain and synthetic mooring lines of Floating Offshore Wind Turbinesdoi:10.18710/LSVFOLSakaris, ChristosSchlanbusch, RuneLee, Chern FongOng, Muk ChenTutkun, MuratNygaard, Tor AndersNORCE Research ASInstitute for Energy (IFE)University of StavangerSchlanbusch, RuneStavangerKjeller3DFloatSIMAGrant number: 312486DataverseNODataverseNOSakaris, ChristosSakaris, Christos2026-02-12EngineeringMooring linesChainSyntheticAccelerationDamageAIMWindAbout 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.2023-03-312023-03-312023-04-012023-04-012023-03-312023-03-312023-04-012023-04-01Simulated acceleration data<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>Christos Sakaris, Nikolaos Anastasiadis, Rune Schlanbusch, Surya Kandukuri, Sensitivity analysis for multi-measurement points based SHM in the mooring lines of floating offshore wind turbines, in: Proceedings of the 21st European Energy Research Alliance (EERA) DeepWind conference, Trondheim, Norway, 2024. Journal of Physics: Conference Series, 2875, 012032, 2024.Christos Sakaris, Nikolaos Anastasiadis, Rune Schlanbusch, Surya Kandukuri, Sensitivity analysis for multi-measurement points based SHM in the mooring lines of floating offshore wind turbines, in: Proceedings of the 21st European Energy Research Alliance (EERA) DeepWind conference, Trondheim, Norway, 2024. Journal of Physics: Conference Series, 2875, 012032, 2024.Nikolaos Anastasiadis, Christos Sakaris, Rune Schlanbusch, John Sakellariou, Vibration-Based SHM in the Synthetic Mooring Lines of the Semi-Submersible OO-Star Wind Floater Under Varying Environmental and Operational Conditions. Sensors, 24(2), 543, 2024.10.3390/s24020543Nikolaos Anastasiadis, Christos Sakaris, Rune Schlanbusch, John Sakellariou, Vibration-Based SHM in the Synthetic Mooring Lines of the Semi-Submersible OO-Star Wind Floater Under Varying Environmental and Operational Conditions. Sensors, 24(2), 543, 2024.Christos Sakaris, Rune Schlanbusch, Tor Anders Nygaard, John Sakellariou, Murat Tutkun, Statistical times series based damage detection in the fiber rope mooring lines of the semi-submersible OO-STAR wind floater, in: Proceedings of the 62nd IEEE Conference on Decision and Control (CDC), Marina Bay Sands, Singapore, 2023, pp. 4861-4866.10.1109/CDC49753.2023.10383638Christos Sakaris, Rune Schlanbusch, Tor Anders Nygaard, John Sakellariou, Murat Tutkun, Statistical times series based damage detection in the fiber rope mooring lines of the semi-submersible OO-STAR wind floater, in: Proceedings of the 62nd IEEE Conference on Decision and Control (CDC), Marina Bay Sands, Singapore, 2023, pp. 4861-4866.Xenophon Konstantinou, Kyriakos Kritikakos, Chern Fong Lee, Christos Sakaris, John Sakellariou, Rune Schlanbusch, Muk Chen Ong, Vibration-based structural health monitoring of the mooring lines in a floating offshore wind turbine under varying environmental conditions: NN vs STS based methods, in: Proceedings of the 31st International Conference on Noise and Vibration Engineering (ISMA), Leuven, Belgium, 2024.979-8-3313-0812-4Xenophon Konstantinou, Kyriakos Kritikakos, Chern Fong Lee, Christos Sakaris, John Sakellariou, Rune Schlanbusch, Muk Chen Ong, Vibration-based structural health monitoring of the mooring lines in a floating offshore wind turbine under varying environmental conditions: NN vs STS based methods, in: Proceedings of the 31st International Conference on Noise and Vibration Engineering (ISMA), Leuven, Belgium, 2024.Chain mooring lines.pdfPDF archive describing the data in the zipped archive "Chain mooring lines"application/pdfChain mooring lines.zipSimulated acceleration data based on healthy and damaged chain mooring lineapplication/zipREAD ME.txttext/plainSynthetic mooring lines.pdfPDF archive describing the data in the zipped archive "Synthetic mooring lines.zip"application/pdfSynthetic mooring lines.zipSimulated acceleration data based on healthy and damaged synthetic mooring lineapplication/zip