Abstract
Deterioration processes such as fatigue and corrosion are typically affecting offshore structures. To "control" this deterioration, inspection and maintenance activities are developed. Probabilistic methodologies represent an important tool to identify the suitable strategy to inspect and control the deterioration in structures such as offshore wind turbines (OWT). Besides these methods, the integration of condition monitoring information (CMI) can optimize the mitigation activities as an updating tool.
In this paper, a framework for risk-based inspection and maintenance planning (RBI) is applied for OWT incorporating CMI, addressing this analysis to fatigue prone details in welded steel joints at jacket or tripod steel support structures for offshore wind turbines. The increase of turbulence in wind farms is taken into account by using a code-based turbulence model. Further, additional modes t integrate CMI in the RBI approach for optimal planning of inspection and maintenance.
As part of the results, the life cycle reliabilities and inspection times are calculated, showing that earlier inspections are needed at in-wind farm sites. This is expected due to the wake turbulence increasing the wind load. With the integration of CMI by means Bayesian inference, a slightly change of first inspection times are coming up, influenced by the reduction of the uncertainty and harsher or milder external agents.
In this paper, a framework for risk-based inspection and maintenance planning (RBI) is applied for OWT incorporating CMI, addressing this analysis to fatigue prone details in welded steel joints at jacket or tripod steel support structures for offshore wind turbines. The increase of turbulence in wind farms is taken into account by using a code-based turbulence model. Further, additional modes t integrate CMI in the RBI approach for optimal planning of inspection and maintenance.
As part of the results, the life cycle reliabilities and inspection times are calculated, showing that earlier inspections are needed at in-wind farm sites. This is expected due to the wake turbulence increasing the wind load. With the integration of CMI by means Bayesian inference, a slightly change of first inspection times are coming up, influenced by the reduction of the uncertainty and harsher or milder external agents.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 28th International Conference on Ocean, Offshore and Arctic Engineering : OMAE2009, May 31- June 5, 2009, Honolulu, Hawaii, USA |
| Number of pages | 10 |
| Publisher | American Society of Mechanical Engineers |
| Publication date | 2009 |
| ISBN (Electronic) | 9780791838440 |
| Publication status | Published - 2009 |
| Event | The International Conference on Ocean, Offshore and Arctic Engineering (OMAE) - Honolulu, Hawaii, United States Duration: 31 May 2009 → 5 Jun 2009 Conference number: 28 |
Conference
| Conference | The International Conference on Ocean, Offshore and Arctic Engineering (OMAE) |
|---|---|
| Number | 28 |
| Country/Territory | United States |
| City | Honolulu, Hawaii |
| Period | 31/05/2009 → 05/06/2009 |
Bibliographical note
Published on a cd.Keywords
- Maintenance Planning
- Offshore Wind Turbine