Journal
RELIABILITY ENGINEERING & SYSTEM SAFETY
Volume 228, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2022.108777
Keywords
Floating offshore wind turbine; Failure rate assessment; Reliability analysis; Failure rate correction model
Funding
- European Regional Development Fund through the Interreg Atlantic Area Programme [EAPA 344/2016]
- Portuguese Foundation for Science and Technology(Fundacao para a Ciencia e Tecnologia-FCT) [UIDB/UIDP/00134/2020]
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A model is proposed to assess the failure rates of components of floating offshore wind turbines based on the knowledge of failure data of corresponding structures of onshore wind turbines with sufficient failure data. The results indicate that the failure rates of components of floating offshore wind turbines are higher than those of onshore devices. The model presented contributes to the risk, failure, and reliability analysis and assessment under insufficient data conditions.
A model is proposed to assess the failure rates of components of floating offshore wind turbines based on the knowledge of failure data of corresponding structures of onshore wind turbines with sufficient failure data. A failure rate correction model is first presented to map the relations of failure features between onshore and floating offshore wind turbines. Subsequently, a failure rate analogy model is established to infer the failure rates of elements of support structures that have no correspondence in onshore devices. The results indicate that the failure rates of components of floating offshore wind turbines are higher than those of onshore devices. Accordingly, a Bayesian network is constructed to analyze the failure rate and reliability of the entire floating offshore wind turbine. The uncertainty of the model is investigated to illustrate the factors that significantly affect the predicted failure rates and reliability. Moreover, the performance of the proposed model is validated by a comprehensive comparison with the existing studies and models. The model presented contributes to the risk, failure, and reliability analysis and assessment under insufficient data conditions.
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