A Multivariate Spatiotemporal Feature Fusion Network for Wind Turbine Gearbox Condition Monitoring

Shixian Dai, Shuang Han, Xinjian Bai, Zijian Kang and Yongqian Liu ()
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Shixian Dai: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of New Energy, North China Electric Power University, Beijing 102206, China
Shuang Han: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of New Energy, North China Electric Power University, Beijing 102206, China
Xinjian Bai: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of New Energy, North China Electric Power University, Beijing 102206, China
Zijian Kang: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of New Energy, North China Electric Power University, Beijing 102206, China
Yongqian Liu: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of New Energy, North China Electric Power University, Beijing 102206, China

Energies, 2025, vol. 18, issue 5, 1-22

Abstract: 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.

Keywords: wind turbine gearbox; graph convolutional network; temporal convolutional network; long short-term memory network; anomaly detection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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