Conducting inspections on wind blade components is crucial for ensuring the overall performance and durability of wind-powered systems. These inspections play a critical role in identifying defects that can result in decreased energy output or pose a risk  to people’s lives. As the world increasingly embraces renewable energy, the demand for wind turbine technicians is expected to rise significantly in the next decade.

Wind turbine inspections are challenging and hazardous due to height and complexity of the component. Manual inspections involve operators using UT method, passing a small sensor along the 86 m blade, taking around 80 h. In the last years, autonomous solutions based on robotics have been introduced in the market to increase the automation of these inspections. While these systems bring notable benefits such as improved productivity and reduced inspection time, adopting a human-centric approach is crucial. In fact, the ultimate responsibility for analyzing the obtained results and detecting critical defects still falls on the operator without any support. This responsibility can be incredibly stressful, considering that the entire functioning of a blade often rests on the quality department or even a single operator.

Targeting challenge 2 ‘RoboticAssistance for heavy duty tasks in the production machines/tool sector’ The ORION project addresses this critical challenge by introducing new capabilities to the actual state-of-the-art robotic inspection systems. IoT solutions will be developed to assist the operator in analysing the large amount of UT data generated during inspections in a structured and user-friendly manner. Additionally, AI algorithms will be developed to aid the operator in decision-making, particularly in situations where the interpretation of obtained signals is key in determining the component’s condition. In addition, ORION enhances the role of humans by placing the operator at the centre of decision-making, taking their expertise in the AI algorithms when their predictions do not meet the accuracy demanded by quality standards. ENDITY Solutions and Savvy Data Systems will get the support from MONDRAGON, specifically their expertise in the building block 16 ‘Deep learning based industrial quality inspection methodology’, methodology highly valuable in developing flexible and robust industrial inspection systems