Abstract Summary
The design of offshore wind turbines is driven by the fatigue limit state; critical fatigue details are often situated around or below the mudline, and particular interest lies with the fatigue accumulation at these locations. Performing dynamic response measurements below the mudline is a challenging task, largely related to the high costs of equipping a turbine at those locations as well as the risk of sensor failure during the installation process. However, measuring this data is extremely valuable for both fatigue assessments and validating model and data driven virtual sensing approaches that do not rely on sensors installed so close to the mudline. During the past decades fiber Bragg grating (FBG) based optical strain sensors have become more commonplace for fatigue monitoring applications of large structures. Despite many advantages over conventional strain gauges, in some cases FBG measurements can become corrupted by sudden, repeated, jumps in the signal, which manifest as spikes or step-like offsets in the data. These jumps are often referred to as peak-splitting. This particular type of failure mode is especially problematic when the data is intended for fatigue assessments, as the artificial jumps will introduce artificial stress ranges to the Rainflow-counting results, and thereby artificially inflate the computed damage. The data can easily be corrupted to a degree where the time domain data as well as damage related measures become un-usable. The latter has motivated the development of a reconstruction tool which removes peak splitting artefacts from the measurement signals. However the method also introduces a quasi-static drift, which can complicate the use in a fatigue damage oriented setting. For this contribution, a long-term measurement campaign, suffering from severe peak splitting on some of the optical strain sensors, will be used to demonstrate the potential of recovering severely damaged data. The data originates from the subsoil region of an operational offshore wind turbine foundation and has been recorded using 4 multiplexed FBGs. Furthermore, conventional strain gauges near interface level are available for reference purposes. The goal of this recovery is to make the data useable again for damage-based assessments. One month of corrupted FBG data has been processed using the peak splitting removal tool. Subsequently, damage equivalent moment (DEM) values have been computed for the corrupted FBG strain data, the conventional strain gauge data, as well as the reconstructed FBG strain data. Comparing the correlations in the computed DEM time series shows that, despite the heavy peak splitting, the corrupted data still contains physical information, and that a scale factor approach could be leveraged to make the data useable again. The scale factor is grating specific and has been derived using the time domain reconstructions. The results of this contribution show that FBGs with severe peak splitting, can still contain valuable information, and might be usable for damage related applications, as well as many time domain applications which are not concerned with the quasi static response, or accept a certain tolerance towards errors in the latter response regime.
