F1 - Analysis of remote sensing and condition monitoring data
F - Operations, Management, Maintenance and Safety
As the use of remote sensing and remote condition monitoring through digital twin technology becomes more commonplace, improved techniques for analysis of the data using AI and machine learning will be needed.
New analysis tools are needed to be developed, demonstrated and benchmarked for analysis of the data generated from remote sensing and remote monitoring using autonomous systems.
Context And Need
Effective and efficient prognostic condition monitoring techniques are needed in order to utilise the state of the art computational capability in its full breadth for the ORE sector.
The size and quantity of data from ORE assets steadily increases. In order to make best and efficient use of this information, tools and standardised processes are needed to handle and interpret these important data sets.
The increasing quantity and value of ORE assets requires and Improve understanding and estimation of O&M activities/cost/scheduling. Risk-based approaches can help to estimate the likelihood and consequence.
To minimise offshore operations and inform O&M strategies, remote sensing and remote condition monitoring through digital twin technology are important developments that have the potential to enable remote resets and repairs and significantly reduce the cost of O&M offshore. Improved analysis tools and treatment of big data generated by remote sensing, proven by smarter benchmarking will be needed to unlock the potential for use of AI and machine learning and present opportunities for advanced asset monitoring and management, potentially lowering OPEX cost, whilst increasing availabilities.
These techniques have the potential to reduce OPEX whilst maintaining acceptable safety levels
- Reduced OPEX (short-term)
- Reduced CAPEX (long-term)
If O&M uncertainties can be reduced the overall OPEX cost and production losses can be reduced.
Digital twins used in other industries
AI/Machine learning established in computational science, but emerging as offshore application
Isolated initiatives to collect and generate benchmark data, e.g. ORE Catapult SPARTA project for reliability
Several O&M models in use, many are deterministic / empirical and would benefit from probabilistic methods.
Following projects are related:
- HOME-Offshore: Holistic Operation and Maintenance for Energy from Offshore Wind Farms (EPSRC, EP/P009743/1) - This project investigates the use of predictive modelling, robotics, advanced sensors and big data techniques to target interventions and thus improve safety, and reduce the cost, of the operation and maintenance of offshore wind farms. This will also help address the increasing shortage of skilled workers in this field. www.homeoffshore.org
Links to Industry Priorities:
Also Offshore Wind Innovation Hub - O&M and Windfarm Lifecycle innovation priorities
We would also like to invite UK researchers and industry stakeholders within ORE to submit links to research projects, both past and present, for inclusion within the landscape.
Therefore, if you have a UK-based research project within an area of ORE that you feel is relevant to a specific research theme or challenge within the Research Landscape, click HERE to submit your research project to the research landscape