Wind energy plays a pivotal role in the energy transition, however, a major obstacle for offshore wind farms remains the high Operations and Maintenance (O&M) costs, which can be up to 35% of the levelized cost of energy (LCOE). Traditional maintenance strategies often result in excessive downtime, increased costs, or premature component replacements. To counter these problems, condition-based maintenance (CBM) uses real-time data to estimate machine health and guide maintenance decisions. Although most CBM studies address either health assessment or maintenance optimisation in isolation, this thesis proposes an end-to-end CBM framework that derives health indicators (HIs) and integrates them into a multiple-threshold CBM framework.
The framework is validated on rotor bearing failures from the CARE data set, which supplies real-world Supervisory Control and Data Acquisition (SCADA) measurements. Rotor bearings, despite their relatively low failure frequencies, cause high downtime and exhibit a prolonged degradation pattern. HIs are extracted with a long- and short-term memory (LSTM) autoencoder (AE) trained solely on healthy turbines to learn normal behaviour. On avarage, the resulting HIs identify anomalies 195 days before failure.
These HIs are fed into a CBM strategy that prioritises timely, minor interventions over costly lastminute replacements. Compared to a purely corrective replacement policy, the strategy reduces annual maintenance expenditure on rotor bearings by an average of 62.5%, mainly by prolonging the useful life of the components and reducing downtime. This thesis therefore demonstrates the value of rotor-bearing HIs, derived from widely available SCADA data, for wind turbine maintenance.