This paper reviews and implements several techniques that can be used for condition monitoring and failure prediction on wind turbines using SCADA data. The focus lies on techniques that answer to requirements of the industry, e.g. robustness, transparency, computationally efficiency, and maintainability. The end result of this research is a pipeline that can accurately detect three types of failures, i.e. generator bearing failures, generator fan failures, and generator stator failures.

This paper presents a data-driven framework for modeling erosion damage based on real blade inspections and mesoscale weather data. The outcome of the framework is a machine-learning-based model that can predict and/or forecast leading-edge erosion damage based on weather data and user-specified wind turbine characteristics. The model output fits directly into the damage terminology used by the industry and can therefore support site-specific maintenance planning and scheduling of repairs.

This paper is the second in a two-part study on lubrication in wind turbine main bearings. Investigations are conducted concerning lubrication in the double-row spherical roller main bearing of a 1.5 MW wind turbine. This includes effects relating to temperature, starvation, grease-thickener interactions and possible non-steady EHL effects. Results predict that the modelled main bearing would be expected to operate under mixed lubrication conditions for a non-negligible proportion of its life.

In order to find consensus between wind energy producers and seismologists, we study the possibility of reducing wind turbine noise recorded at seismological stations. We find that drilling half-circular holes in front of the wind turbines helps to reduce the seismic noise. We also study the influence of topographic effects on seismic noise reduction.

This work presents a method to use low-noise IEPE sensors in the low-frequency range down to 0.05 Hz. In order to achieve phase and amplitude accuracy with this type of sensor in the low-frequency range, a new calibration procedure for this frequency range was developed. The calibration enables the use of the low-noise IEPE sensors for large structures, such as wind turbines. The calibrated sensors can be used for wind turbine monitoring, such as fatigue monitoring.