A Bearing Fault Diagnosis Method Based on a Residual Network and a Gated Recurrent Unit under Time-Varying Working Conditions

The diagnosis of bearing faults is an important guarantee for the healthy operation of mechanical equipment. Due to the time-varying working conditions of mechanical equipment, it is necessary to achieve bearing fault diagnosis under time-varying working conditions. However, the superposition of the two-dimensional working conditions of speed and acceleration brings great difficulties to diagnosis via data-driven models. The long short-term memory (LSTM) model based on the infinitesimal method is an effective method to solve this problem, but its performance still has certain limitations. On this basis, this article proposes a model for fault diagnosis under time-varying operating conditions that combines a residual network model (ResNet) and a gate recurrent unit (model) (GRU). Firstly, the samples were segmented, and feature extraction was performed using ResNet. We then used GRU to process the information. Finally, the classification results were output through the output network. This model could ignore the influence of acceleration and achieve high fault diagnosis accuracy under time-varying working conditions. In addition, we used t-SNE to reduce the dimensionality of the features and analyzed the role of each layer in the model. Experiments showed that this method had a better performance compared with existing bearing fault diagnosis methods.