Amid the global energy transition, offshore wind energy has been positioned as a pivotal technology,
utilizing both bottom-fixed (BOWT) and floating offshore wind turbine (FOWT) configurations. Offshore wind turbines face high operation and maintenance (O&M) costs, representing up to 35% of the levelized cost of energy (LCOE), with the drivetrain and especially the main bearing (MB) being among the most failure-prone components, leading to long downtime and reduced energy production. In floating wind turbines, main bearings experience additional challenges due to combined wind and wave loads and the platform motions.

This research investigates the differences in loading and fatigue damage of the upwind main bearing (MB1) in a monopile BOWT and semi-submersible FOWT, both based on the DTU 10 MW reference wind turbine (RWT). Using OpenFAST simulations under Design Load Case (DLC) 1.2, according to Standard IEC 61400-1: 2019, this study calculates the MB1 loads for both wind turbine configurations, at a North Sea location east of Scotland, with a water depth of 60 m. Simulations are conducted across the full operating wind speed range (4.5 - 24.5 m/s), and loads are calculated through an analytical formulation. The axial and radial loads of MB1 are post-processed according to Standard ISO 281:2007, to compute the dynamic equivalent load, which is used to evaluate the fatigue damage of the main bearings. The Load Duration Distribution (LDD) method, which defines the load cycles, and the site-specific Weibull distribution are used to estimate the hourly fatigue damage of MB1, which is then extrapolated to a 20-year design lifetime, yielding the time-dependent Remaining Useful Life (RUL).

A comparative analysis of MB1 load means and standard deviations reveals that, for the examined
drivetrain configuration, radial loads dominate the main bearing loading and fatigue contribution in both configurations. Specifically, for the FOWT, they represent 74 to 90% of the dynamic equivalent load and 39 to 72% of the fatigue damage across all operating wind speeds. For the BOWT configuration, the corresponding ranges are 75 to 90% and 42 to 72%, respectively. MB1 of the FOWT experiences higher axial and radial loads, mainly due to platform surge and pitch motions, which increase rotor thrust and amplify the axial loading, as well as platform sway, roll and yaw motions that mainly amplify the radial loads. The largest load differences between FOWT and BOWT are encountered at around the rated wind speed of 11.4 m/s, and they peak at 13.5 m/s, where the FOWT axial load is 13.17% higher than in the BOWT.

Over the 20-year lifetime, MB1 in the FOWT accumulates 5.03% more fatigue damage, failing at 11.98 years, compared to 12.58 years for the BOWT. BOWT MB1 exhibits higher fatigue damage in the below-rated wind speed region, especially for wind speeds below 8.5 m/s. For aligned wind-wave cases, the reduction in the MB1 damage for both FOWTs and BOWTs is only 0.6% compared to realistic (misaligned) cases, indicating that, the wind-wave misalignment for wind speeds in the above-rated region, has a slight influence on the cumulative damage. A parametric sensitivity analysis shows that turbulence intensity is the most influential environmental factor: a change from Class A to C results in up to 10% reduction in MB1 damage. Among system parameters, platform mass significantly affects MB1 fatigue in FOWTs, with a 20% increase in mass reducing damage by 5.6%, while a 20% decrease increases it by 4.4%.

Overall, the findings of this thesis prove that MB loads in floating wind turbines are subjected to greater complexity and amplification, due to platform motions and aerodynamic, hydrodynamic, structural and control coupling. This results in increased fatigue damage and indicates the importance of advanced fatigue-aware, system-integrated control strategies that consider the platform dynamics to ensure main bearing reliability in future floating wind developments. The study concludes with recommendations for future work, including the validation of the results with field data, the exploration of alternative drivetrain layouts and the implementation of advanced control schemes to mitigate the impact of the platform motion. ... Read more

Rotor imbalances—such as mass imbalance, pitch misalignment, and yaw misalignment—are critical faults in wind turbine systems. These imbalances cause uneven load distribution on components, leading to excessive wear, failures, increased operational costs due to unplanned downtime, and reduced energy output. Despite advancements in monitoring technologies, current maintenance strategies in wind turbines still rely on time-based manual inspections, as they lack reliable automated detection systems. This thesis addresses the need for a more efficient fault detection framework by integrating already available drivetrain Condition Monitoring System (CMS) vibration signals— commonly used to detect drivetrain component failures, like in gears and bearings— with traditional SCADA data. The aim is to extract signal features that capture the system's dynamic behavior and effectively detect and diagnose rotor imbalances. Notably, this approach overcomes the limitation of current systems not having direct measurements from the blades by leveraging operational data already collected from wind turbines.

Building on prior research, the proposed approach combines frequency and time-domain analyses and focuses on two key data sources: drivetrain vibration measurements and rotor speed data from the SCADA system. A decoupled simulation framework integrates aeroelastic simulations from OpenFAST with a multi-body drivetrain model in SIMPACK, specifically for the 10 MW DTU reference wind turbine. The results show that drivetrain velocity signals, particularly in the side-to-side direction, are highly sensitive to rotor imbalances, enabling accurate trend analysis. Features such as peak amplitudes at 1P and 3P frequencies form the basis of the fault detection and diagnosis criteria proposed in this thesis. By using the median values of their distributions, imbalances can be effectively detected and diagnosed. This approach also supports the implementation of a decision tree framework for real-time fault classification across various operating conditions.

The methodology was tested under both above and below-rated wind speeds, first in steady-state conditions and then in turbulent inflow scenarios. Additionally, health state indicators are proposed to recognize fault severity levels by clustering median value features within predefined ranges for low, medium, and high severity. This comprehensive monitoring approach effectively tracks fault progression across the imbalance scenarios under study. As a result, the proposed method lays the foundation for a future data-driven system that can reduce reliance on manual inspections and provide a scalable solution for predictive maintenance in wind turbine operations. ... Read more