PhD Title - Machine Learning in Wind Turbine O&M
My PhD involves analysing the various data that is produced by wind turbines in massive volumes. The data, either SCADA or high frequency vibration, is then analysed through the use of machine learning techniques. These techniques are varied in their application, and particular to the data-type. My PhD is split up into four different work-packages. The first two explore the use of anomaly detection for two different failure cases. The third examines oil history data of gearboxes for remaining useful life prediction. The final work-package explores deep-learning for high-frequency vibration data as a way of removing the need for feature extraction. Overall, this PhD covers the area of Machine Learning for Wind Energy.
Email - firstname.lastname@example.org
Start Date - October 2017
Degree - MSci Physics, University of Glasgow. MSc Wind Energy Systems, University of Strathclyde
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