## 3 Dynamics Simulation and Experimental Verification
### Experiment Setup  
Four distinct health states' bearing data are included in the simulation dataset: health state (N), fault width 0.5 mm (IF), fault width 1 mm (OF), and fault width 2 mm (R). We classified the bearing states into four categories based on the fault width. Each state of the bearing contains 236 samples, and each sample contains 1024 data points. To capture the highest correlation among samples in the time series, a sliding window with a 0.5 overlap ratio is applied. The dataset is then partitioned into training, validation, and test sets in a 7:2:1 ratio. The experimental setting environment is as follows: The GPU is NVIDIA RTX 4090 (NVIDIA Corporation, Santa Clara, CA, USA), the CPU is Intel i9-14900 KF (Intel Corporation, Santa Clara, CA, USA), and the running environment is the python3.8 pytorch framework. The model is trained with an initial learning rate of 0.001, over 50 epochs, using a batch size of 32. The bearing data were gathered using the high-speed bearing-rotor test platform depicted in Figure 7 in order to confirm the correctness of the suggested model. According to the requirements of the experimental verification, Table 1 provides a thorough overview of the data used. The test platform is composed of a laser acquisition instrument, a data acquisition instrument, a drive motor and a bearing test platform. The vibration of the system was measured using a German Polytec OFV-505 (Polytec GmbH, Waldbronn, Germany) non-contact laser vibrometer. Data acquisition was performed with a HIOKI MR8875-30 data logger (Hioki E.E. Corporation, Nagano, Japan). The sampling frequency was 20 kHz. The rated power of the motor is 11 kW, the maximum current of the motor is 22.3 A, and the rated torque of the motor is 7 N\(\cdot\)m. The speed was set to 2000 r/min. The model's generality was confirmed by extracting the simulation and experimental signals, and the data can also further validate the model. Due to limitations in experimental conditions, signals collected experimentally contain more noise than simulated signals. Figure 8 displays the model's accuracy curve. When the training begins, when the quantity of iterations rises, the model continuously optimizes until it converges. When the model passes through the 20 th epoch, the accuracy rate can be stably above 95%, and the loss function shows a downward trend, proving the robustness of the method we proposed. Furthermore, we have also drawn the confusion matrix of the classification task and the  
Figure 7: Experiment platform for bearing–rotor system. t-SNE visualization classification diagram, as shown in Figure 9. As shown in Figure 9, the method proposed in this paper achieves high classification accuracy. This further demonstrates the robustness and effectiveness of the proposed approach. Extensive training was performed using simulation and experimental data to determine hyperparameter values. Their specific values are shown in Table 2.  
To further verify the generalization of the proposed method under actual operating conditions, radial loads of 200 N, 500 N and 1000 N were applied to the bearing using an experiment platform. Data collected under these three different radial loads were used for validation. As shown in Figure 10, confusion matrices and t-SNE visualization plots show that our method has excellent generalization ability. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Healthy State** & **Label** & **Sample Number** \\ \hline N & 0 & 238 \\ IR & 1 & 236 \\ OR & 2 & 235 \\ R & 3 & 237 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Introduction to data details. Figure 8: Model accuracy curve. Figure 9: (**a**) The model confusion matrix; (**b**) the t-SNE visualization diagram.