Fault Diagnosis Based on Interpretable Convolutional Temporal-Spatial Attention Network for Offshore Wind Turbines

Abstract

Fault diagnosis (FD) for offshore wind turbines (WTs) are instrumental to their operation and maintenance (O&M). To improve the FD effect in the very early stage, a condition monitoring based sample set mining method from super-visory control and data acquisition (SCADA) time-series data is proposed. Then, based on the convolutional neural network (CNN) and attention mechanism, an interpretable convolutional temporal-spatial attention network (CTSAN) model is proposed. The proposed CTSAN model can extract deep temporal-spatial features from SCADA time-series data sequentially by: ① a convolution feature extraction module to extract features based on time intervals; ② a spatial attention module to extract spatial features considering the weights of different features; and ③ a temporal attention module to extract temporal features considering the weights of intervals. The proposed CT-SAN model has the superiority of interpretability by exposing the deep temporal-spatial features extracted in a human-understandable form of the temporal-spatial attention weights. The effectiveness and superiority of the proposed CTSAN model are verified by real offshore wind farms in China.