A novel fault diagnosis framework of rolling bearings based on adaptive dynamic activation convolutional capsule network

This paper presents a fault diagnosis framework of rolling bearings based on the adaptive dynamic activation convolutional capsule network (CN). The CN is first used to vectorize and mine the spatial information of features aiming at extracting more comprehensive spatial location features. Then, the feature extraction layer of the CN is improved to extract deeper features and reduce the number of parameters. The dynamic activation function is then introduced to extract features better than the steady-state activation function, which can self-adapt the activation features and capture variable feature information. Finally, real rolling bearing data sets are used to verify the superiority and effectiveness of the proposed method with the assistance of comparisons with existing fault diagnosis methods. The results confirmed that the proposed framework has better performance in terms of accuracy and generalization.