PhD Fellow in Experimental Modeling of Human Error in Wind Turbine Maintenance - OsloMet

Your main duties and areas of responsibility

Wind turbines are central to the green transition, yet their operational reliability depends heavily on effective and safe inspection and maintenance. Current maintenance approaches rely primarily on turbine sensor data, meaning that human and organizational factors are largely overlooked. This represents a significant safety gap, as human error contributes to nearly 40% of wind turbine failures and a significant share of severe accidents during maintenance activities. Such errors are frequently associated with technicians' performance shaping factors (PSFs), such as fatigue, cognitive workload, task complexity, and challenging environmental conditions. The main objective of this PhD project is to reduce human error during wind turbine maintenance by experimentally modelling these factors. Through controlled experiments using Virtual Reality (VR) simulations, combined with physiological sensors (such as fNIRS, IMUs, and heart rate monitors), the candidate will collect multimodal data on the technicians' states. Machine learning will then be used to analyze this data and develop models that can predict the probability of human error. The results will lay the scientific foundation for a human-centered approach to maintenance aligned with Industry 5.0, contributing to the design of safer and more reliable maintenance strategies. As a PhD Fellow at OsloMet, you have the opportunity to apply for funding for extended research stays abroad

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