Fault diagnosis of wind turbine based on multi-signal CNN-GRU model
Yang Chen, Xiaoxia Zheng
Abstract
Deep Learning has been widely used in the monitoring and diagnosis of wind turbines. However, most of the current fault diagnosis methods only use single sensor signal as the input of DL model, which leads to the limitation of the model performance. Therefore, this paper proposes a multi-signal CNN-GRU model. Firstly, the acquired multiple sensor signals are converted to time–frequency images by Multi-Synchrosqueezing S-Transform, the frequency domain features of multiple sensors are extracted by Convolutional Neural Network and fused by Attention Mechanism, then the multi-source time-frequency features are extracted by Gated Recurrent Unit and finally classified by SoftMax. Experiments are conducted on the CWRU dataset and the field gearbox dataset. The results show that the proposed method achieves an average accuracy of 99.69% and 100% on the two datasets, which are both higher than existing DL-based fault diagnosis methods. The proposed method can effectively fuse signals from multiple sensors, thus improving the classification accuracy and stability of the model, which has high practicality and reliability for fault diagnosis of wind turbines.