Spatial‐temporal attention and GRU based interpretable condition monitoring of offshore wind turbine gearboxes

Abstract

Abstract Effective monitoring and early warning of gearbox operating status are of great significance to the operation and maintenance (O&M) of offshore wind turbines (WTs). This study proposes a normal behaviour modelling (NBM) method based on the spatial‐temporal attention module and the gated recurrent unit (GRU), for the condition monitoring of offshore WT gearboxes. The proposed method has a superior performance by extracting the spatial and temporal features from the supervisory control and data acquisition (SCADA) system, and also has the unique advantage of model interpretability. Specifically, in the NBM training stage, the spatial features of offshore wind farm SCADA data are extracted by the spatial attention module firstly. Then, the temporal features of the spatial feature sequences above are extracted and fused by the GRU network. Afterwards, the temporal attention module is applied to strengthen the expression of key time points. In the NBM testing stage, the output residual between the predicted and the measured values is calculated and monitored by the exponential weighted moving average (EWMA) control chart. Finally, the effectiveness and superiority of the proposed NBM method are verified by detailed simulations on the Donghai Bridge offshore wind farm.