Adaptive weight-based capsule neural network for bearing fault diagnosis

Abstract

In industrial systems, the vibration signals of rolling bearings are highly complex due to varying operating conditions and ambient noise. Presented with signals with a lack of features after being disturbed by a complex environment, traditional convolutional neural networks cannot diagnose bearing faults accurately and effectively. This paper proposes a dynamic capsule network (DCCN) based on adaptive shared weights to solve the above problems. To learn the features of the vibration signals, the convolution weights are adaptively adjusted and shared to different convolutional layers through an attention mechanism, which can effectively reduce the computational cost of the network. In addition, a dynamic routing algorithm is used to generate sub-capsules that share weights. These sub-capsules extract and transform the fault features into vector features information storage to reduce feature loss. Finally, the feature-extraction capability of the DCCN is enhanced by concatenating the dynamic convolution using skip connect lines. The effectiveness of the proposed method is verified by noise and variable-load experiments, and the DCCN exhibits a better generalization and diagnosis performance than current advanced deep-learning methods.