Digital Twin Assisted Degradation Assessment of Bearing Cage Performance

The construction of a digital twin model for the full life cycle of rolling bearings is of great significance for analyzing their degradation performance and health management. However, existing researches primarily concentrate on the degradation of the outer ring of bearings. The cage, as an important component of bearings, lacks extensive research. Therefore, this article proposes a digital twin assisted assessment method for the degradation of bearing cages. First, a dynamic model including bearing cage fracture is established to generate simulation degradation signals. Second, the simulation signal is modified based on the squeeze and excitation cycle generative adversarial network (SECycleGAN) to minimize the characteristic distribution differences between the simulation and real signals. Finally, the corrected high-fidelity signal is used to train the proposed selective kernel transformer (SKformer) model to assess the degradation stage of the bearing cage. This model can simultaneously capture the long-range temporal correlation features and local mutation multiscale features of the input signals, thus improving the model's recognition ability and generalization performance. The effectiveness of the proposed method is demonstrated through signals collected on real and open-source bearing cage degradation test rigs. The results indicate that the proposed method can produce high-fidelity bearing cage degradation signals and achieve better classification accuracy with limited data.