Development of digital models of different components that are part of an offshore renewable energy facility aimed at optimizing maintenance tasks
Offshore renewable technologies for energy conversion have a high potential but they still need to improve their cost-competitiveness. Accurate condition monitoring would enable predictive and preventive operation and maintenance processes, being crucial the realization of virtual models of power plants. Deep knowledge of potential failures and the right tools to detect and locate failures are necessary.
In this direction, the development of digital physical hybrid models (digital twins) of equipment and components of offshore facilities will allow to evaluate the behavior of these systems along their useful life depending on the conditions of use. In this way, predictive and preventive operation and maintenance activities will be optimized, increasing the reliability as well as the life of the assets.
More in detail, these hybrid models will include:
- Modeling of components and equipment based on normal-operation data.
- Modeling of components and equipment under fault conditions. Characterizations of the physical degradation of components (cause and effect).
- Data fusion technologies: digital model fed with experimental data, field data or fault simulations.
The development of these hybrid models will allow to accelerate the development of new designs adapted to real use, by:
- Prediction of failures based on real data entries and real conditions of use.
- Prediction of behavior when facing failures.
- Prediction of operation against unexpected events.
- Product optimization to extend the useful life, including repairs.
- Obtaining new information not accessible nowadays (not measurable).
- Offer new services in the life phase of the component.
- Improvement of the overall cost efficiency.
Contact and more information: Pablo Eguia