This work proposes a novel concept for a semi-autonomous human-cyber-physical system (HCPS) to operate next-generation wind turbines on the way towards Industry 5.0. The exponential increase in the complexity of future wind turbines requires artificial intelligence (AI) to operate the machines efficiently and consistently. Evolving current Industry 4.0 digital twin technology beyond a sole aid for a human decision-making process, the digital twin in the proposed system is utilized for highly effective training of the AI through machine learning. Human intelligence (HI) is elevated to a supervisory level, making high-level decisions through a human-machine interface to break the autonomy when needed. This paper points out a plausible way to realize the HCPS by identifying strategic development demands for the key enabling technologies projected from readily available knowledge.

This study develops a methodology to create detailed visual Digital Twins of large-scale structures with their realistic damages detected from visual inspection or nondestructive testing (NDT). The methodology is demonstrated with a transition piece of an offshore wind turbine and a composite rotor blade, with surface paint damage and subsurface delamination damage, respectively. Artificial Intelligence and color threshold segmentation are used to classify and localize damages from optical images taken by drones. These damages are digitalized and mapped to a three-dimensional geometry reconstruction of the large-scale structure or a CAD model of the structure. To map the images from 2D to 3D, metadata information is combined with the geo placement of the large-scale structure’s 3D model. After mapping the damage, the Digital Twin gives an accurate representation of the structure. The location, shape, and size of the damage are visible on the Digital Twin. The demonstrated methodology can be applied to industrial sectors such as wind energy, the oil and gas industry, marine and aerospace to facilitate asset management.