A novel hierarchical failure analysis approach targeting the operation and maintenance of floating offshore wind turbines
This paper proposes a novel failure analysis approach, hierarchical Failure Mode and Effects Analysis (FMEA), aimed at identifying critical components during the operation and maintenance phase of floating offshore wind turbines . The system’s degradation process is categorized into four stages based on maintenance actions, and a three-level Cost Priority Number calculation framework is developed to quantify cost-related risks across these stages. By integrating a maintenance model, this approach incorporates the uncertainties of maintenance strategies into the criticality analysis of components and transforms maintenance data into input parameters for evaluation indices. The objective is to address the limitations of traditional cost-based methods in representing maintenance processes and the challenges posed by insufficient maintenance data. Overall, the proposed approach offers targeted insights to optimize maintenance strategies, enhancing the operation and maintenance of floating offshore wind farms . • A hierarchical FMEA approach is proposed for offshore wind turbine risk analysis. • Present novel CPN calculation framework for maintenance processes reflection. • Use maintenance model outputs to reflect strategy impact on failures and costs. • Validate approach through criticality rankings in two maintenance strategies. • Provide effective guidance for maintenance strategies through failure analysis.
Authors
- Ronghua Zhu (ORCID: https://orcid.org/0000-0003-4211-3364)
- Zijian Tao (ORCID: https://orcid.org/0009-0000-7970-3574)
- Mingchuan Wang (ORCID: https://orcid.org/0000-0002-9867-258X)
- Chizhong Wang (ORCID: https://orcid.org/0000-0003-0234-7254)
- Qinghai Chen
- Jiajun Hu (ORCID: https://orcid.org/0000-0002-4420-1133)
Institutions
- Zhejiang Ocean University (CN)
- Guangzhou Education Bureau (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Renewable Energy
- Published
- 2024-12-31
- DOI
- https://doi.org/10.1016/j.renene.2024.122267
- Citations
- 11
- Primary Topic
- Machine Fault Diagnosis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.61