Fine-grained fault diagnosis methods for offshore wind turbine gearboxes driven by artificial intelligence—by multi-source data analysis and deep learning
DOI:
https://doi.org/10.4108/ew.14193Keywords:
offshore wind power, gearbox, multi-source data fusion, artificial intelligence, deep learning, intelligent fault diagnosisAbstract
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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Copyright (c) 2026 Hongfeng Chen, Chao Chen, Xingdu Li, Huihui Han, Xinfa Shi, Xin Wang
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