Maintenance Planning of Offshore Wind Turbine using Condition Monitoring Information
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...
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American Society of Mechanical Engineers
2009
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Online Access: | https://vbn.aau.dk/da/publications/363c6dfa-d3db-4080-aa54-e57acd7d16ad https://vbn.aau.dk/ws/files/55336005/Maintenance_Planning_of_Offshore_Wind_Turbine_using_Condition_Monitoring_Information |
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ftalborgunivpubl:oai:pure.atira.dk:publications/363c6dfa-d3db-4080-aa54-e57acd7d16ad 2024-09-15T17:50:23+00:00 Maintenance Planning of Offshore Wind Turbine using Condition Monitoring Information Ramírez, José G. Rangel Sørensen, John Dalsgaard 2009 application/pdf https://vbn.aau.dk/da/publications/363c6dfa-d3db-4080-aa54-e57acd7d16ad https://vbn.aau.dk/ws/files/55336005/Maintenance_Planning_of_Offshore_Wind_Turbine_using_Condition_Monitoring_Information eng eng American Society of Mechanical Engineers https://vbn.aau.dk/da/publications/363c6dfa-d3db-4080-aa54-e57acd7d16ad urn:ISBN:9780791838440 info:eu-repo/semantics/openAccess Ramírez , J G R & Sørensen , J D 2009 , Maintenance Planning of Offshore Wind Turbine using Condition Monitoring Information . in Proceedings of the 28 th International Conference on Ocean, Offshore and Arctic Engineering : OMAE2009, May 31- June 5, 2009, Honolulu, Hawaii, USA . American Society of Mechanical Engineers , The International Conference on Ocean, Offshore and Arctic Engineering (OMAE) , Honolulu, Hawaii , United States , 31/05/2009 . Maintenance Planning Offshore Wind Turbine contributionToPeriodical 2009 ftalborgunivpubl 2024-08-06T23:52:00Z 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. 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 ... Article in Journal/Newspaper Arctic Aalborg University's Research Portal |
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Aalborg University's Research Portal |
op_collection_id |
ftalborgunivpubl |
language |
English |
topic |
Maintenance Planning Offshore Wind Turbine |
spellingShingle |
Maintenance Planning Offshore Wind Turbine Ramírez, José G. Rangel Sørensen, John Dalsgaard Maintenance Planning of Offshore Wind Turbine using Condition Monitoring Information |
topic_facet |
Maintenance Planning Offshore Wind Turbine |
description |
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. 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 ... |
format |
Article in Journal/Newspaper |
author |
Ramírez, José G. Rangel Sørensen, John Dalsgaard |
author_facet |
Ramírez, José G. Rangel Sørensen, John Dalsgaard |
author_sort |
Ramírez, José G. Rangel |
title |
Maintenance Planning of Offshore Wind Turbine using Condition Monitoring Information |
title_short |
Maintenance Planning of Offshore Wind Turbine using Condition Monitoring Information |
title_full |
Maintenance Planning of Offshore Wind Turbine using Condition Monitoring Information |
title_fullStr |
Maintenance Planning of Offshore Wind Turbine using Condition Monitoring Information |
title_full_unstemmed |
Maintenance Planning of Offshore Wind Turbine using Condition Monitoring Information |
title_sort |
maintenance planning of offshore wind turbine using condition monitoring information |
publisher |
American Society of Mechanical Engineers |
publishDate |
2009 |
url |
https://vbn.aau.dk/da/publications/363c6dfa-d3db-4080-aa54-e57acd7d16ad https://vbn.aau.dk/ws/files/55336005/Maintenance_Planning_of_Offshore_Wind_Turbine_using_Condition_Monitoring_Information |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Ramírez , J G R & Sørensen , J D 2009 , Maintenance Planning of Offshore Wind Turbine using Condition Monitoring Information . in Proceedings of the 28 th International Conference on Ocean, Offshore and Arctic Engineering : OMAE2009, May 31- June 5, 2009, Honolulu, Hawaii, USA . American Society of Mechanical Engineers , The International Conference on Ocean, Offshore and Arctic Engineering (OMAE) , Honolulu, Hawaii , United States , 31/05/2009 . |
op_relation |
https://vbn.aau.dk/da/publications/363c6dfa-d3db-4080-aa54-e57acd7d16ad urn:ISBN:9780791838440 |
op_rights |
info:eu-repo/semantics/openAccess |
_version_ |
1810292212829782016 |