A Multivariate Spatiotemporal Feature Fusion Network for Wind Turbine Gearbox Condition Monitoring
SCADA data, due to their easy accessibility and low cost, have been widely applied in wind turbine gearbox condition monitoring. However, the high-dimensional and nonlinear nature of the collected data, along with the insufficient spatiotemporal feature capabilities of existing methods and the lack...
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MDPI AG
2025-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/5/1273 |
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| author | Shixian Dai Shuang Han Xinjian Bai Zijian Kang Yongqian Liu |
| author_facet | Shixian Dai Shuang Han Xinjian Bai Zijian Kang Yongqian Liu |
| author_sort | Shixian Dai |
| collection | DOAJ |
| description | SCADA data, due to their easy accessibility and low cost, have been widely applied in wind turbine gearbox condition monitoring. However, the high-dimensional and nonlinear nature of the collected data, along with the insufficient spatiotemporal feature capabilities of existing methods and the lack of consideration of the physical mechanisms of wind turbine operation, limit the accuracy of monitoring models. In this paper, a multivariate spatiotemporal feature fusion network is proposed for wind turbine gearbox condition monitoring. First, by analyzing the operational mechanism of wind turbines and the correlation between sensor data, the time series data are transformed into graph data. Then, graph convolutional networks and temporal convolutional networks are used to extract spatial and temporal features, respectively. Next, long short-term memory networks are employed to fuse the extracted temporal and spatial features, further capturing long-term spatiotemporal dependencies. Finally, the proposed method is validated using real data from two wind turbines. Experimental results show that the proposed method reduces the RMSE by 29.67% and 17.61% compared to the best-performing models. Moreover, the proposed method provides early warning signals 188.6 h and 133.67 h in advance, achieving stable and efficient early anomaly detection for wind turbines. |
| format | Article |
| id | doaj-art-861cca4eeecc4e62af6726d513d036c0 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-861cca4eeecc4e62af6726d513d036c02025-08-20T02:53:19ZengMDPI AGEnergies1996-10732025-03-01185127310.3390/en18051273A Multivariate Spatiotemporal Feature Fusion Network for Wind Turbine Gearbox Condition MonitoringShixian Dai0Shuang Han1Xinjian Bai2Zijian Kang3Yongqian Liu4State 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, 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, ChinaSCADA data, due to their easy accessibility and low cost, have been widely applied in wind turbine gearbox condition monitoring. However, the high-dimensional and nonlinear nature of the collected data, along with the insufficient spatiotemporal feature capabilities of existing methods and the lack of consideration of the physical mechanisms of wind turbine operation, limit the accuracy of monitoring models. In this paper, a multivariate spatiotemporal feature fusion network is proposed for wind turbine gearbox condition monitoring. First, by analyzing the operational mechanism of wind turbines and the correlation between sensor data, the time series data are transformed into graph data. Then, graph convolutional networks and temporal convolutional networks are used to extract spatial and temporal features, respectively. Next, long short-term memory networks are employed to fuse the extracted temporal and spatial features, further capturing long-term spatiotemporal dependencies. Finally, the proposed method is validated using real data from two wind turbines. Experimental results show that the proposed method reduces the RMSE by 29.67% and 17.61% compared to the best-performing models. Moreover, the proposed method provides early warning signals 188.6 h and 133.67 h in advance, achieving stable and efficient early anomaly detection for wind turbines.https://www.mdpi.com/1996-1073/18/5/1273wind turbine gearboxgraph convolutional networktemporal convolutional networklong short-term memory networkanomaly detection |
| spellingShingle | Shixian Dai Shuang Han Xinjian Bai Zijian Kang Yongqian Liu A Multivariate Spatiotemporal Feature Fusion Network for Wind Turbine Gearbox Condition Monitoring Energies wind turbine gearbox graph convolutional network temporal convolutional network long short-term memory network anomaly detection |
| title | A Multivariate Spatiotemporal Feature Fusion Network for Wind Turbine Gearbox Condition Monitoring |
| title_full | A Multivariate Spatiotemporal Feature Fusion Network for Wind Turbine Gearbox Condition Monitoring |
| title_fullStr | A Multivariate Spatiotemporal Feature Fusion Network for Wind Turbine Gearbox Condition Monitoring |
| title_full_unstemmed | A Multivariate Spatiotemporal Feature Fusion Network for Wind Turbine Gearbox Condition Monitoring |
| title_short | A Multivariate Spatiotemporal Feature Fusion Network for Wind Turbine Gearbox Condition Monitoring |
| title_sort | multivariate spatiotemporal feature fusion network for wind turbine gearbox condition monitoring |
| topic | wind turbine gearbox graph convolutional network temporal convolutional network long short-term memory network anomaly detection |
| url | https://www.mdpi.com/1996-1073/18/5/1273 |
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