Offshore wind turbines are exposed to extreme weather conditions: deep water, environmental influences and resistance to rust place high demands on the technology. Various coatings and protective systems are already in place to protect the outer shell of the towers under water and the surface of the offshore wind turbines. Nevertheless, the inspection, maintenance and repair of the coatings is still very time-consuming and costly. Therefore, within the scope of the IsyMoo project, five partners from industry and research are engaged in the development of innovative autonomous inspection techniques. In future, drones, remote-operated vehicles (ROVs), sensors and electronic data management and communication technologies will be able to automatically evaluate the condition of wind turbines. This will make expensive diving operations unnecessary.

The aim of the project, which was launched in June 2018, is to enhance a networked, data-based maintenance model which includes the use of innovative solutions. Among other things, sensors such as cathodic protection systems and vibration sensors will be used. Sensors integrated into the coating, for example, can register material changes and transmit the information to a drone or ROV. Finally, the control centre is notified by these sensors and simultaneously receives the corresponding images from the thermal imaging cameras. Machine learning enables more precise information about the condition of the wind turbines to be continually transmitted. This makes maintenance more predictable and less expensive.

The BMWi is funding the IsyMoo project with 1.3 million euros until the end of May 2021.