The existing unbalance identification algorithm without trial weight adopted an optimization algorithm framework and approximated the optimal solution through numerous iterative operations. However， such strategies typically faced the limitations of slow convergence speed and the tendency to fall into local extrema. Therefore， neural networks were used to directly learn and analyze the complex mapping relationship between unbalance vibration response and unbalance， thus realizing high-precision unbalance identification. A sufficient unbalance vibration dataset with labels was constructed by simulating the rotor dynamics model. A feature fusion mechanism was designed to address the multi-dimensional complex-valued characteristics of unbalanced data. At the core algorithm level， a CNN-GRU hybrid model was constructed. In this model， CNN was responsible for extracting local spatial features from vibration data， while GRU captured temporal dependencies within the vibration data. By integrating information from both spatial and temporal domains， the model’s generalization ability and recognition accuracy were significantly enhanced. The unbalance recognition results of test set data and experimental bench demonstrate that this method may accurately predict the unbalance of the rotors， providing a rapid and accurate guide for dynamic balancing in the field without trial weights.
