In his doctoral dissertation MSc (Tech) Panagiotis Korkos explored data driven fault detection for the hydraulic pitch system of multi megawatt wind turbines using only standard Supervisory Control and Data Acquisition (SCADA) data. He analysed ten years of operational data from a Finnish wind farm, systematically collected frequent pitch system failures, and developed supervised AI-based models whose performance was promising and robust across varying operating conditions. The developed methodology provides a low cost alternative to conventional high frequency condition monitoring systems, as it relies solely on SCADA data while still enabling real time fault diagnosis. Wind farm operators, maintenance planners, and asset managers can use this approach to enable predictive maintenance, reduce Operation and Maintenance (O&M) costs, and extend wind turbine lifetime.
The doctoral dissertation of MSc (Tech) Panagiotis Korkos in the field of Materials Science and Engineering titled AI-based Fault Detection in Hydraulic Pitch System of Wind Turbines will be publicly examined at the Faculty of Engineering and Natural Sciences at Tampere University on 20 March 2026.
The Opponents will be Research Professor (emeritus) David Infield from the University of Strathclyde in United Kingdom and Professor Konstantinos Gryllias from the KU Leuven in Belgium. The Custos will be Professor Mikko Hokka form the Faculty of Engineering and Natural Sciences.