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

Journal of Modern Power Systems and Clean Energy(2024)

引用 0|浏览6
Fault diagnosis of offshore wind turbines is instrumental to its operation and maintenance. To effectively diagnose faults in the very early stage, this study firstly proposes a condition monitoring based sample mining method from SCADA time-series data. Then, based on the convolutional neural network and attention mechanism, an interpretable convolutional temporal-spatial attention network (CTSAN) model is proposed. The proposal can extract deep temporal-spatial features from SCADA data sequentially by (1) a convolution module to extract features based on time intervals, (2) a spatial attention module to extract spatial features considering their weights, and (3) a temporal attention module to extract temporal features considering the weights of intervals. The proposal has the superiority of interpretability by exposing the deep temporal-spatial features extracted in a human-understandable form of temporal-spatial attention weights. The effectiveness and superiority of the proposal are verified by numerical studies on a real offshore wind farm in China.
Offshore wind turbine,gearbox,fault diagnosis,attention mechanism,interpretability,temporal-spatial features
AI 理解论文
Chat Paper