Digital Twin for Floating Offshore Wind Foundations Operation and Maintenance Management
In the offshore industry, environmental conditions typically have a significant effect on operation performance, maintenance, and total costs. In this study, predictive maintenance tools were developed in the context of a floating testbed structure (FTB), a cylindrical buoy with features representat...
| Published in: | Volume 2: Structures, Safety, and Reliability |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
American Society of Mechanical Engineers (ASME)
2024
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| Subjects: | |
| Online Access: | https://cris.vtt.fi/en/publications/15d36e93-5ab0-484c-b51d-3789613c9d14 https://doi.org/10.1115/OMAE2024-124269 |
| Summary: | In the offshore industry, environmental conditions typically have a significant effect on operation performance, maintenance, and total costs. In this study, predictive maintenance tools were developed in the context of a floating testbed structure (FTB), a cylindrical buoy with features representative of the maintenance needs of floating offshore wind foundations. The FTB was measured dynamically and statistically over the local weather conditions at a harbor in Portugal. A digital twin of the FTB was constructed using the data to assess mooring line loads and hydrodynamic responses at a different place in the Atlantic Ocean. One major factor restricting operation and maintenance is exposure to dynamic motion. A limiting criterion was demonstrated for heavy manual work based on short-length moving root-mean-square (RMS) values of vibration, acceleration, and roll/pitch. For operational state, history, and statistical analysis, RMS values were profiled as to time-at-level to evaluate the probability of a maintenance weather window and the suitability for maintenance deployment. FTB digital twin data was used to analyze mooring line loading and remaining useful life (RUL) to provide deterministic decision-making support for justified inspections and structural health monitoring (SHM) under different marine sea state conditions. The approaches shown can also be used for flagging sea-state conditions that are leading to premature failures and reduced lifetime. |
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