Research Article
Fine-grained fault diagnosis methods for offshore wind turbine gearboxes driven by artificial intelligence—by multi-source data analysis and deep learning
@ARTICLE{10.4108/ew.14193, author={Hongfeng Chen and Chao Chen and Xingdu Li and Huihui Han and Xinfa Shi and Xin Wang}, title={Fine-grained fault diagnosis methods for offshore wind turbine gearboxes driven by artificial intelligence---by multi-source data analysis and deep learning}, journal={EAI Endorsed Transactions on Energy Web}, volume={13}, number={1}, publisher={EAI}, journal_a={EW}, year={2026}, month={8}, keywords={offshore wind power, gearbox, multi-source data fusion, artificial intelligence, deep learning, intelligent fault diagnosis}, doi={10.4108/ew.14193} }- Hongfeng Chen
Chao Chen
Xingdu Li
Huihui Han
Xinfa Shi
Xin Wang
Year: 2026
Fine-grained fault diagnosis methods for offshore wind turbine gearboxes driven by artificial intelligence—by multi-source data analysis and deep learning
EW
EAI
DOI: 10.4108/ew.14193
Abstract
INTRODUCTION: Offshore wind turbine gearboxes operate under complex conditions and are highly prone to faults. Traditional single-source diagnostic methods are sensitive to noise and load fluctuations, limiting diagnostic reliability. OBJECTIVES: This study aims to improve the diagnostic accuracy and recognition performance of gearbox fault categories. METHODS: Multi-source monitoring data were preprocessed and fused, followed by feature optimization and construction of a deep learning-based diagnosis model for fault detection and classification. RESULTS: The proposed model achieved accuracies of 0.951, 0.947, and 0.938 under different load conditions, with Area Under the Curve AUC values above 0.96, outperforming benchmark models. CONCLUSION: The proposed method improves diagnostic accuracy, robustness, and engineering applicability for offshore wind turbine gearbox fault diagnosis.
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