FLOATSense: A Time-Series Dataset and Benchmark for Fatigue Load Reconstruction on Floating Offshore Wind Turbine Towers

Fatigue limits the lifetime of an offshore wind turbine tower and is tracked from the bending moment along it, yet strain gauges are rare and cover one section, while a tower-top accelerometer is standard. Reconstructing the moment at every height from it is a time-series problem, and no public benchmark scores it by fatigue damage. We introduce FLOATSense, a dataset and benchmark for fatigue load reconstruction on 22 MW floating offshore wind turbines: 717,948 time series at 10 Hz from the 19,404 OpenFAST simulations of FLOATBench on three tower geometries, with accelerometer and SCADA inputs and the fore-aft moment at eleven heights as target. A reconstruction is scored by the fatigue damage it implies, not by waveform error, and the six realizations of an operating point are kept together, so a model that reads only the condition gets no credit on the within-condition metric. Twenty learned models and an adapted field-validated physics method are evaluated within tower, across towers and under sensor ablation. Sensor-level leaderboards mislead: base log-damage $R^2$ saturates among the leading models and its leaders collapse at the top, where at the declared budget only three learned models beat a one-gain floor and a condition-only control given the true operating condition exceeds them all in aggregate $R^2$, so realization tracking must be read within condition; longer training recovers the top for the two TCNs, not for the tested attention and state-space models; five labeled simulations of a new tower recover its base by fine-tuning and its top by one scale per height, which fine-tuning often misses; and a second accelerometer axis improves the top at every budget. Limitations: the campaign is simulated, wind and waves are aligned, and only the fore-aft moment is scored. Resources: data and code at https://anonymous.4open.science/w/FLOATSense-56B2/review/index.html.