Fault Diagnosis Based on Interpretable Convolutional Temporal-spatial Attention Network for Offshore Wind Turbines
Xiangjing Su, Chao Deng, Yanhao Shan, Farhad Shahnia, Yang Fu, Zhaoyang Dong
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.