Arild Bergesen Husebø

Arild Bergesen Husebø is a Research Fellow affiliated with the Department of Engineering Sciences at the University of Agder (UiA). His work focuses on applying machine learning techniques to the condition monitoring of electromechanical machinery, particularly in fault diagnosis and predictive maintenance.

Education:

• Bachelor's and Master's degrees in Renewable Energy from UiA

Research Interests:
His research spans mechatronics, renewable energy systems, and industrial automation, with a strong emphasis on leveraging machine learning for early fault detection in machinery. Key areas include bearing diagnostics, resonance frequency analysis, and electrical fault identification in induction motors.

Recent Publications:
His studies highlight advancements in time-domain signal processing, convolutional neural networks, and autoencoder-based feature extraction for predictive maintenance. These works align with applications in electrical machines, wind turbines, and industrial systems.