Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network

Junshuai Yan, Yongqian Liu (), Xiaoying Ren and Li Li
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Junshuai Yan: School of New Energy, North China Electric Power University, Beijing 102206, China
Yongqian Liu: School of New Energy, North China Electric Power University, Beijing 102206, China
Xiaoying Ren: School of New Energy, North China Electric Power University, Beijing 102206, China
Li Li: School of New Energy, North China Electric Power University, Beijing 102206, China

Energies, 2023, vol. 16, issue 19, 1-22

Abstract: Gearbox fault deterioration can significantly impact the safety, reliability, and efficiency of wind turbines, resulting in substantial economic losses for wind farms. However, current condition monitoring methods face challenges in effectively mining the hidden spatio-temporal features within SCADA data and establishing reasonable weight allocations for model input variables. To tackle these issues, we proposed a novel condition monitoring method for wind turbine gearboxes called HBCE, which integrated a feature-time hybrid attention mechanism (HA), the bidirectional convolutional long short-term memory networks (BiConvLSTM), and an improved exponentially weighted moving-average (iEWMA). Specifically, utilizing historical health SCADA data acquired through the modified Thompson tau data-cleaning algorithm, a normal behavior model (HA-BiConvLSTM) of gearbox was constructed to effectively extract the spatio-temporal features and learn normal behavior patterns. An iEWMA-based outlier detection approach was employed to set dynamic adaptive thresholds, and real-time monitor the prediction residuals of HA-BiConvLSTM to identify the early faults of gearbox. The proposed HBCE method was validated through actual gearbox faults and compared with conventional spatio-temporal models (i.e., CNN-LSTM and CNN&LSTM). The results illustrated that the constructed HA-BiConvLSTM model achieved superior prediction precision in terms of RMSE, MAE, MAPE, and R 2 , and the proposed method HBCE can effectively and reliably identify early anomalies of a wind turbine gearbox in advance.

Keywords: wind turbine gearbox; condition monitoring; attention mechanism; convolutional long short-term memory network; adaptive threshold (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: 2023
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