Research Assistant Dr. Ifigeneia Antoniadou and Professor Keith Worden, from the ENL project team, won the best paper award in the 7th European Workshop on Structural Health Monitoring in Nantes, France in July. Their paper on the use of a spatially adaptive thresholding method for the condition monitoring of a wind turbine gearbox proposes an adaptive unsupervised learning method for the feature discrimination part of the gearbox condition monitoring. The threshold in this case is determined by the data and therefore a better recovery of the functions actually occurring with the data can be accomplished. In addition, it was also demonstrated how the method can be applied to features selected from a time-frequency analysis. The method was tested on actual wind turbine gearbox datasets and the results seemed to be improved when compared to older techniques.