Abstract:When the traditional fault diagnosis method is used to judge the bearing fault, there are some problems such as insufficient feature extraction, incomplete use of time sequence characteristics, and complicated calculation. In order to solve these problems, a combined diagnosis method based on a multi-scale convolutional neural network (MCNN), bidirectional gated recurrent unit (BiGRU), and attention mechanism is proposed in this study. Firstly, MCNN is used to extract multi-scale features from signal data, which realizes further extraction of features in terms of spatial level. Secondly, the BiGRU network is used to obtain sequential relations from positive and negative directions in terms of time level. Next, an attention mechanism is introduced to ignore some information that is not relevant to the results and reduce the risk of information loss to improve accuracy. After mapping is created through the full connection layer, the bearing fault diagnosis is finally completed through the Softmax classification method. In this study, LSTM model, GRU model, SVM model, CNN-BiGRU model, and other models are compared through experiments, and the experimental results show that the proposed model has higher accuracy than the previous models. The accuracy of fault diagnosis in a single working condition reaches 98.1%, and the average accuracy of fault diagnosis in multiple working conditions reaches 97.8%.