A Model-data Combined Driven Vibration Digital Twin Model for Magnetically Suspended Motor

Magnetic bearing avoids power loss and wear caused by contact friction, and greatly improves the motor's energy conversion efficiency and service life. However, the vibration caused by rotor mass imbalance would not only lead to performance decline, but also easily lead to system instability, and even cause safety accidents. In order to analyze the dynamic characteristic and the vibration mechanism, a model-data combined driven digital twin model of motor is proposed. The twin model takes both time and space scales into account, and reveals the dynamic mapping between vibration source and vibration response. With verifying the consistency of vibration signals predicted in digital space and measured in physical space, the effectiveness of proposed method is proved.