Abstract: There was overreliance on experts’ knowledge when extracting time domain signals by the traditional fault diagnosis method of rolling bearings, and the fault information was expressed inadequately by features. Aiming at the problems,an intelligent fault diagnosis model based on residual network and capsule network was proposed. Firstly, raw vibration signal was used as input, and the onedimensional convolution neural network was used to extract global features from the time domain signal , and then the residual network was used to extract the low-level features of the data, and they were sent to the capsule network to vectorize the low-level features, after that the lowlevel features were combined into advanced features and classified through dynamic routing process which was improved by fuzzy clustering. Finally, in order to verify the effectiveness of this method, the proposed method was tested through the rolling bearing data sets,and the diagnosis result of this method was compared with the diagnosis result of other deep learning methods. The research results indicate that the residual capsule network reaches 99.95% in classification accuracy, and the convergence speed has been improved. The t-distributed stochastic neighbor embedding(tsne) visible analysis further verifies that the network model has the ability to self-adaptively mine high-level features. The residual capsule network possesses good accuracy and generalization in the fault diagnosis of rolling bearings.

Key words: rolling bearing;fault diagnosis;deep learning;residual network;capsule network;fuzzy clustering