Population based structural health monitoring is a low-cost monitoring campaign. The cost reduction from this type of digital enabled asset management tool is manifested by sharing information, in this case a wind farm foundation, within the population. By sharing the information in the wind farm this reduces the amount of sensors and physical model updating, reducing the cost of the monitoring campaign.

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.