A novel transfer diagnosis method under unbalanced sample based on discrete-peak joint attention enhancement mechanism

摘要

Deep learning has been widely used in intelligent fault diagnosis field in the era of industrial internet due to its excellent big data analysis ability. However, it lacks the specific attention enhancement mechanism in the training process, and is difficult to be applied to the speed transfer fault diagnosis under unbalanced training sample condition. Considering these challenges, a neural network called discrete-peak joint attention enhancement (DPJAE) convolutional model is proposed. First, the proposed discrete channel attention enhancement method is applied to enhance the characteristic discreteness of convolutional neural network channels. Then, the peak channel attention enhancement method follows, which is employed to enhance feature peaks in convolutional neural network channels. Finally, the discrete-peak joint attention enhancement method is placed in the middle of a well-designed robust CNN architecture to enhance the discreteness and peak value for channel characteristics. In order to avoid the over-fitting phenomenon of high variance in the training process of the deep network model, regularization method is introduced to regularize the weights for the front layer network. The diagnosis results on the open test dataset show that the DPJAE model not only gets high accuracy in the unbalanced training samples, but also achieves high performance in the rotating speed transfer testing samples. The proposed method is also verified on the private dataset. Compared with other diagnostic methods, the method has better diagnostic results.