## 4 Case Study and Verification  
### Introduction to Experiment  
The experimental data are from a rolling bearing fault experimental platform, as shown in Figure 7a, and the bearing used for testing is an N205 EM cylindrical roller bearing. The single point faults were processed on the bearing using electric discharge technology, including the inner ring, outer ring, and roller element faults, as shown in Figure 7b-e. The fault diameters were 0.3, 0.6, and 1 mm, respectively. The tested bearing is installed inside the bearing seat, and three-axis acceleration sensors 1A313E are installed on the seat, as shown in Figure 7. The sampling frequency of the vibration signals is 20 kHz. To simulate the different working conditions, the tested bearing load was set to 0, 30, and 60 kg, and the driving motor speed was set to 800, 1200, and 1500 r/ min. The vibration signal of each fault diameter was collected by our vibration testing equipment. According to the speed conditions, datasets A, B, and C are used to verify the effectiveness of intelligent transfer learning.  
In this test, the bearing's rotation frequency ranged from 800 to 1500 r/min. Between 800 and 1500 points were sampled in each revolution. To better illustrate the vibration characteristics of each revolution, 1200 points were selected as one sample with a step size of 256. Furthermore, in order to improve and enhance the effectiveness and resolution of the time-frequency analysis, each sample has 1200 points, which provides better time-frequency characteristics. Four hundred samples were collected from each fault vibration signal, and a total of 5200 samples were obtained. Considering the cross-validation experiment, each dataset was divided into the training and testing sets at a 3:1 ratio. Thus, the training set has a total of 3900 samples, and the testing set has a total of 1300 samples. The detailed information of this experimental data is shown in Table 1. As an example, the fault vibration signals with a damage diameter of 0.3 mm and 800 r/min on different components are shown in Figure 8a-d. According to the proposed signal decomposition and reconstruct method, the denoised vibration signals of different faults are shown in Figure 8e-h. The comparison of the signal-to-noise (SNR) ratio of the signals in Table 2 shows that the SNR is 12.45, and the root mean square is 0.44, indicating a good noise-reduction effect. Then, after applying the CWT and histogram equalization methods, 400 enhanced time-frequency maps were obtained for each state. The time-frequency representations before and after the  
Figure 7: Testing rig of rolling bearing faults and fault bearings.  
enhancement of the bearing signal are shown in Figure 9. The enhanced time-frequency maps have good clustering and a strong local feature prominence. Here, the computer configuration is the Inter Intel (R) Core (TM) i7-10510U CPU and 16 GB RAM in window 11 and Matlab 2022b.  
Figure 8: Vibration signal of roller table bearing under different health conditions. (**a**) Normal\(-\)condition vibration signal of roller table. (**b**) Original vibration signal of inner race fault with 1 mm. (**c**) Original vibration signal of ball fault with 1 mm. (**d**) Original vibration signal of out race fault with 1 mm. (**e**) Denoised normal\(-\)condition vibration signal of roller table. (**f**) Denoised original vibration signal of inner race fault with 1 mm. (**h**) Denoised original vibration signal of ball fault with 1 mm. (**h**) Denoised original vibration signal of out race fault with 1 mm.