A failure knowledge graph learning framework for offshore wind turbines with incomplete knowledge
Year: 2025
Type: article
Abstract: This study presents a novel framework for Failure Knowledge Graph (FKG) construction tailored for the safe operation and maintenance of offshore wind turbines . Specifically, Bidirectional Encoder Representations from Transformers (BERT) and Conditional Random Field (CRF) are combined for failure extraction, enhanced by iterative learning for failure data transfer from onshore to offshore wind turbines. Additionally, this framework incorporates a rule-based pseudo-label module and an innovative replacement-based pseudo-sample module to mitigate the impact of label errors and failure data imbalance during the iterative learning process. With the failure events extracted, the affiliate components and corresponding failure modes are identified to construct a tree-structured FKG automatically for offshore wind turbines . The feasibility and effectiveness of the proposed framework are validated by the presentation of an FKG regarding 313 offshore wind turbines recorded in the LGS-offshore dataset. Overall, the study provides the offshore wind sector with an intelligent framework for failure data analysis, presentation, and understanding and contributes to the safe operation of offshore wind turbines and wind farms .
Authors: Yi Ding, Feng Zhu, He Li, Ajith Kumar Parlikad, Min Xie
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,220
Funders: UK Research and Innovation, National Natural Science Foundation of China, Research Grants Council, University Grants Committee, Innovation and Technology Commission, Innovation and Technology Commission - Hong Kong, HORIZON EUROPE Framework Programme, Engineering and Physical Sciences Research Council, HORIZON EUROPE Marie Sklodowska-Curie Actions
OpenAlex ID: W4408190911