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.