Abstract: In the process of bearing fault diagnosis of wind turbine, the fault diagnosis method based on deep learning is limited by limited labeled samples, which has problems such as difficulties in model convergence and low recognition accuracy. For this purpose,a parallel convolutional neural network (P-CNN) and feature fusion-based fault diagnosis method for small sample wind turbine bearings was proposed. Firstly, the vibration signal of the bearing was decomposed into several intrinsic mode functions (IMF) components and residual components by ensemble empirical mode decomposition (EEMD). Then, the short time Fourier transform (STFT) was performed on them, and they were respectively converted into timefrequency characteristic maps, and multiple identical convolutional neural network branches were constructed as feature extractors. Finally, the extracted timefrequency domain features were fused in the fusion layer and used as the input of the final classifier to achieve fault identification of wind turbine bearings, the applicability and effectiveness of this method was validated using different size bearing datasets from Case Western Reserve University. The results show that the parallel convolutional neural network (PCNN) and feature fusionbased fault diagnosis method has an average accuracy of 94.5% when containing only 160 samples, which has higher accuracy and stronger robustness compared to support vector machine(SVM)、FaultNet and deep convolutional neural networks with wide firstlayer kernel(WDCNN).
Key words:  deep learning; ensemble empirical mode decomposition(EEMD); shorttime Fourier transform(STFT); parallel convolutional neural network(PCNN); feature extraction; intrinsic mode functions (IMF); accuracy and robustness of fault diagnosis