Panagiotis Korkos

I received my diploma degree (Integrated Master) in Mechanical Engineering and Aeronautics from University of Patras, Greece with high honors in 2018. My research focuses on implementing Artificial intelligence and statistical processing techniques in order to detect and identify faults related to the pitch system of wind turbines. Also my PhD project aims to develop physics-based models to determine the root cause of the most common failures in this system which can be either tribological or hydraulic. In addition, I am interested in digital twins, combining data science and physics-based simulations of complex systems.

• Machine learning algorithms
• Deep learning
• Time series analysis
• Condition monitoring
• Condition based maintenance
• Predictive maintenance
• Fault detection and classification

Doctoral School of Industry Innovation, K.F. and Maria Dunderberg Foundation - K.F. ja Maria Dunderbergin testamenttisäätiö

Korkos P., Kleemola J., Linjama M., Lehtovaara, A., "Representation Learning for Detecting the Faults in a Wind Turbine Hydraulic Pitch System Using Deep Learning ", Energies (2022), 15(24), 9279 

Korkos P., Kleemola J., Linjama M., Lehtovaara, A., "Fault Detection in a Wind Turbine Hydraulic Pitch System Using Deep Autoencoder Extracted Features ", PHM Society European Conference (2022), 7(1), 261–268. 

Korkos P., Linjama M., Kleemola J., Lehtovaara A., "Data annotation and feature extraction in fault detection in a wind turbine hydraulic pitch system ", Renewable Energy 185 (2022), 692-703