An improved GNN using dynamic graph embedding mechanism: A novel end-to-end framework for rolling bearing fault diagnosis under variable working conditions

Abstract

Traditional deep learning (DL)-based rolling bearing fault diagnosis methods usually use signals collected under specific working condition to train the diagnosis models. This may lead to the lack of domain adaptive ability of these trained models, thus making it difficult to obtain satisfactory diagnosis accuracy when working conditions fluctuate. To address it, a novel fault diagnosis framework based on the graph neural network (GNN) and dynamic graph embedding mechanism (DGE) was proposed in this paper. Firstly, convolutional neural network (CNN) is used to extract the hidden fault features from raw bearing vibration signals. Secondly, DGE module is designed with edge dropout mechanism to transform the features exacted by CNN into higher-level graph-structured features dynamically. Then, GNN is applied to further mine the fault features sensitivity to the fluctuating bearing working conditions. Finally, a novel mechanism named node voters is proposed to replace traditional graph-level attribute update function in GNN to obtain optimal fault pattern recognition results. Experiment results shows that the proposed framework can not only realize the end-to-end fault diagnosis of rolling bearings, but also has excellent domain adaptive ability to obtain better stability and diagnosis accuracy under variable working conditions compared to traditional DL-based methods.