Holistic Operation and Maintenance for Energy from Offshore Wind Farms using advanced drones, AI & advanced modelling (HOME offshore)

Project objective

This project aims to combine drone technology for the robotic inspection of wind turbine, artificial intelligence to discover the complex fault patterns, and intelligent physics modelling to reduce costs and improve performance levels in this rapidly growing industry.

Expected outcome

This project will undertake the research necessary for the remote inspection and asset management of offshore wind farms and their connection to shore. At present most Operation and Maintenance (O&M) is still undertaken manually onsite. Remote monitoring through advanced sensing, robotics, data-mining and physics-of-failure models therefore has significant potential to improve safety and reduce costs. The three key elements are: robotic inspection and advance sensing, artificial intelligence in the form of machine learning, and advanced physics modelling tools. This interactive approach, with drones, learning computers and intelligent modelling all working in synchronisation, will mean that offshore wind farms of the future will be smarter, safer, easier to maintain, and more profitable, ensuring their continued use in the transition away from non-renewable power sources.

Contributing organizations

  • Cranfield University
  • DNV GL
  • Durham University
  • Fugro OCEANOR AS
  • Siemens Open Innovation
  • University of Manchester