## 3 Multi-Adversarial Joint Distribution Adaptation Network
### Single-Working-Condition Transfer Experiment  
The experiment designed six groups of single-working-condition transfer tasks: T0-T1, T0-T2, T0-T3, T1-T2, T1-T3, and T2-T3. The source domain contains 2900 samples, while the target domain contributes 2300 samples for training and an additional 600 samples for testing. In each experiment, the model is trained for 50 epochs, and for each transfer task, the training is repeated 10 times. The accuracy of the 10 training runs is averaged to obtain the final result. The experimental results are shown in Figure 9, where the diagnostic accuracy of the target domain for the transfer tasks T1-T2, T2-T3, and T3-T2 reached 100%, and the diagnostic accuracy for the transfer tasks T1-T3, T1-T0, T2-T0, T3-T0, T2-T1, and T3-T1 exceeded 99%, and for T0-T2 and T0-T3, it was around 98%. The diagnostic accuracy of transferring from condition A to condition B is relatively lower compared to other migration tasks, but it is still above 96%. The experimental results confirm the effectiveness of the MAJDAN model.  
In practical situations, the collected signals mostly contain noise. In order to verify the anti-noise performance of MAJDAN, this paper introduces Gaussian white noise at varying signal-to-noise ratios (SNRs) to the bearing vibration signal. The definition of SNR is as follows:  
\[SNR_{\text{dB}}=10\text{lg}\left(\frac{P_{\text{signal}}}{P_{\text{noise}}}\right) \tag{8}\]  
where \(P_{\text{signal}}\) is the power of the original signal, and \(P_{\text{noise}}\) is the power of the noise. Figure 10 shows the original vibration signal and the vibration signal with SNR = 50 dB, 45 dB, 40 dB, and 35 dB Gaussian noise added.  
The same preprocessing and training methods as above are applied to the signal added with Gaussian white noise. Figure 11 shows the experimental results of the MAJDAN under various transfer tasks after adding noise with different SNRs to the signal.  
Figure 9: Target domain diagnostic accuracy in different transfer tasks.  
Figure 11: Accuracy of different noise intensities in each transfer task.  
Figure 10: Vibration signals with different noise intensities.  
From the experimental results in Figure 11, it can be found that using the MAJDAN on the vibration signal with 35 dB noise can also make the diagnosis accuracy of each transfer task reach more than 85%, indicating that the network has a certain ability to resist noise interference. In order to further verify the superiority of the MAJDAN, this paper takes a sample with 45 dB noise as an example to compare it with other methods: 1D CNN, Domain Adversarial Neural Networks (DANNs) [44], CORAL [27], and deep subdomain adaptation network (DSAN) [45]. Each of the adaptive methods uses the same 1 DLCNN structure as a feature extractor. The results of the comparison are shown in Table 4. The highest diagnostic accuracy for each transfer task is represented in bold. According to the results in the table, it can be found that, the network model used in this paper is more stable than other comparison models on various transfer tasks and has the highest accuracy on most transfer tasks. In a 45 dB noise environment, the average accuracy of the MAJDAN on all tasks is 6-15% higher than other comparison network models.