A CNN-SA-GRU Model with Focal Loss for Fault Diagnosis of Wind Turbine Gearboxes
Gearbox failures are a major cause of unplanned downtime and increased maintenance costs, making accurate diagnosis crucial in ensuring wind turbine reliability and cost-efficiency. However, most existing diagnostic methods fail to fully extract the spatiotemporal features in SCADA data and neglect...
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MDPI AG
2025-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/14/3696 |
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| author | Liqiang Wang Shixian Dai Zijian Kang Shuang Han Guozhen Zhang Yongqian Liu |
| author_facet | Liqiang Wang Shixian Dai Zijian Kang Shuang Han Guozhen Zhang Yongqian Liu |
| author_sort | Liqiang Wang |
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| description | Gearbox failures are a major cause of unplanned downtime and increased maintenance costs, making accurate diagnosis crucial in ensuring wind turbine reliability and cost-efficiency. However, most existing diagnostic methods fail to fully extract the spatiotemporal features in SCADA data and neglect the impact of class imbalance, thereby limiting diagnostic accuracy. To address these challenges, this paper proposes a fault diagnosis model for wind turbine gearboxes based on CNN-SA-GRU and Focal Loss. Specifically, a CNN-SA-GRU network is constructed to extract both spatial and temporal features, in which CNN is employed to extract local spatial features from SCADA data, Shuffle Attention is integrated to efficiently fuse channel and spatial information and enhance spatial representation, and GRU is utilized to capture long-term spatiotemporal dependencies. To mitigate the adverse effects of class imbalance, the conventional cross-entropy loss is replaced with Focal Loss, which assigns higher weights to hard-to-classify fault samples. Finally, the model is validated using real wind farm data. The results show that, compared with the cross-entropy loss, using Focal Loss improves the accuracy and F1 score by an average of 0.24% and 1.03%, respectively. Furthermore, the proposed model outperforms other baseline models with average gains of 0.703% in accuracy and 4.65% in F1 score. |
| format | Article |
| id | doaj-art-06ba614234f948c6a6ca1af1ce067371 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-06ba614234f948c6a6ca1af1ce0673712025-08-20T03:58:31ZengMDPI AGEnergies1996-10732025-07-011814369610.3390/en18143696A CNN-SA-GRU Model with Focal Loss for Fault Diagnosis of Wind Turbine GearboxesLiqiang Wang0Shixian Dai1Zijian Kang2Shuang Han3Guozhen Zhang4Yongqian Liu5Longyuan Power Group Co., Ltd., Beijing 100034, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of New Energy, North China Electric Power University, Beijing 102206, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of New Energy, North China Electric Power University, Beijing 102206, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of New Energy, North China Electric Power University, Beijing 102206, ChinaLongyuan Power Group Co., Ltd., Beijing 100034, ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of New Energy, North China Electric Power University, Beijing 102206, ChinaGearbox failures are a major cause of unplanned downtime and increased maintenance costs, making accurate diagnosis crucial in ensuring wind turbine reliability and cost-efficiency. However, most existing diagnostic methods fail to fully extract the spatiotemporal features in SCADA data and neglect the impact of class imbalance, thereby limiting diagnostic accuracy. To address these challenges, this paper proposes a fault diagnosis model for wind turbine gearboxes based on CNN-SA-GRU and Focal Loss. Specifically, a CNN-SA-GRU network is constructed to extract both spatial and temporal features, in which CNN is employed to extract local spatial features from SCADA data, Shuffle Attention is integrated to efficiently fuse channel and spatial information and enhance spatial representation, and GRU is utilized to capture long-term spatiotemporal dependencies. To mitigate the adverse effects of class imbalance, the conventional cross-entropy loss is replaced with Focal Loss, which assigns higher weights to hard-to-classify fault samples. Finally, the model is validated using real wind farm data. The results show that, compared with the cross-entropy loss, using Focal Loss improves the accuracy and F1 score by an average of 0.24% and 1.03%, respectively. Furthermore, the proposed model outperforms other baseline models with average gains of 0.703% in accuracy and 4.65% in F1 score.https://www.mdpi.com/1996-1073/18/14/3696wind turbineCNNShuffle AttentionGRUFocal Lossfault diagnosis |
| spellingShingle | Liqiang Wang Shixian Dai Zijian Kang Shuang Han Guozhen Zhang Yongqian Liu A CNN-SA-GRU Model with Focal Loss for Fault Diagnosis of Wind Turbine Gearboxes Energies wind turbine CNN Shuffle Attention GRU Focal Loss fault diagnosis |
| title | A CNN-SA-GRU Model with Focal Loss for Fault Diagnosis of Wind Turbine Gearboxes |
| title_full | A CNN-SA-GRU Model with Focal Loss for Fault Diagnosis of Wind Turbine Gearboxes |
| title_fullStr | A CNN-SA-GRU Model with Focal Loss for Fault Diagnosis of Wind Turbine Gearboxes |
| title_full_unstemmed | A CNN-SA-GRU Model with Focal Loss for Fault Diagnosis of Wind Turbine Gearboxes |
| title_short | A CNN-SA-GRU Model with Focal Loss for Fault Diagnosis of Wind Turbine Gearboxes |
| title_sort | cnn sa gru model with focal loss for fault diagnosis of wind turbine gearboxes |
| topic | wind turbine CNN Shuffle Attention GRU Focal Loss fault diagnosis |
| url | https://www.mdpi.com/1996-1073/18/14/3696 |
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