Research on Fault-Diagnosis Technology of Rare-Earth Permanent Magnet Motor Based on Digital Twin

To address the persistent challenges in diagnosing bearing faults, this study proposes an intelligent diagnostic framework based on the principle that mechanical faults manifest as symmetry-breaking phenomena in a system’s vibration signals. In a healthy motor, vibration signals exhibit a high degree of symmetry, whereas faults introduce identifiable and distinct asymmetries. This study constructs a high-fidelity digital twin model based on the five-dimensional model theory to simulate both the symmetrical (healthy) state and various asymmetrical faulty states of motor bearings—specifically, inner race, outer race, and rolling element faults—thereby effectively addressing the critical issue of data scarcity. Building upon this framework, fault features characterizing these asymmetries are accurately extracted using an optimized variational mode decomposition (VMD) algorithm and subsequently classified with a convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM) model. The results validate the model’s ability to accurately replicate bearing-fault data. The proposed diagnostic method achieves a stable and high average accuracy of 98.44 ± 0.41% over multiple runs on the simulation data. Furthermore, its effectiveness was validated on a public real-world bearing dataset, where it achieved an accuracy of over 95%, demonstrating its robustness and potential for industrial applications by effectively identifying fault-induced asymmetries.