# Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method
## 4 Experimental Verification and Analysis
### Experiment II
#### 4.2.3 Analysis of Experimental Results  
The experimental results of the PU bearing data are shown in Table 6. It can be seen that the MPGCTN can achieve the best diagnostic effect in each task. In addition, on the most difficult task \(P_{3-1}\), the highest diagnostic accuracy of the contrast model is 43.05%, while that of the proposed MPGCTN method is 68.12%, an increase of 25.07%. At the same time, it can be seen that some of the comparison models can obtain good results in some tasks, but their diagnostic performance is not stable enough in other tasks, which verifies that the proposed MPGCTN method has good generalization. Based on the above results, it is verified that the proposed method has excellent diagnostic performance in the actual damaged bearing dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Tasks** & **Baseline** & **MMD** & **MKMMD** & **CORAL** & **DANN** & **ADDA** & **MPGCTN** \\ \hline \(P_{0-1}\) & \(33.28\pm 1.59\) & \(42.79\pm 1.18\) & \(46.19\pm 3.56\) & \(33.44\pm 3.40\) & \(52.91\pm 1.43\) & \(65.36\pm 1.28\) & \(75.36\pm 1.17\) \\ \(P_{0-2}\) & \(77.23\pm 1.13\) & \(84.35\pm 2.34\) & \(85.54\pm 1.18\) & \(85.83\pm 2.76\) & \(84.29\pm 1.35\) & \(88.35\pm 1.78\) & \(95.64\pm 1.39\) \\ \(P_{0-3}\) & \(51.38\pm 1.76\) & \(65.31\pm 2.55\) & \(66.84\pm 3.59\) & \(57.32\pm 2.37\) & \(70.25\pm 3.01\) & \(82.65\pm 1.43\) & \(90.78\pm 0.48\) \\ \(P_{1-0}\) & \(42.57\pm 1.35\) & \(48.41\pm 1.48\) & \(56.84\pm 3.59\) & \(62.32\pm 1.57\) & \(52.42\pm 2.51\) & \(62.65\pm 1.76\) & \(70.78\pm 1.08\) \\ \(P_{1-2}\) & \(45.52\pm 1.11\) & \(57.84\pm 3.22\) & \(53.77\pm 3.59\) & \(57.32\pm 1.97\) & \(69.72\pm 2.36\) & \(73.64\pm 1.92\) & \(83.78\pm 0.68\) \\ \(P_{1-3}\) & \(35.89\pm 1.79\) & \(38.56\pm 0.68\) & \(46.24\pm 3.59\) & \(57.32\pm 2.49\) & \(49.14\pm 2.64\) & \(65.65\pm 1.15\) & \(76.78\pm 1.21\) \\ \(P_{2-0}\) & \(70.61\pm 0.86\) & \(80.80\pm 1.35\) & \(76.83\pm 3.59\) & \(57.32\pm 2.07\) & \(81.41\pm 1.58\) & \(82.67\pm 1.48\) & \(93.18\pm 2.08\) \\ \(P_{2-1}\) & \(39.43\pm 1.88\) & \(42.50\pm 1.69\) & \(46.12\pm 3.59\) & \(57.32\pm 2.11\) & \(47.26\pm 2.32\) & \(60.65\pm 1.11\) & \(85.78\pm 0.58\) \\ \(P_{2-3}\) & \(48.97\pm 1.76\) & \(77.40\pm 2.64\) & \(66.97\pm 3.59\) & \(57.32\pm 1.57\) & \(80.21\pm 2.55\) & \(79.65\pm 1.02\) & \(86.78\pm 0.78\) \\ \(P_{3-0}\) & \(54.57\pm 3.35\) & \(50.39\pm 0.98\) & \(57.84\pm 2.32\) & \(54.54\pm 2.64\) & \(50.38\pm 2.19\) & \(77.12\pm 2.99\) & \(88.37\pm 0.98\) \\ \(P_{3-1}\) & \(33.26\pm 3.15\) & \(40.06\pm 1.25\) & \(85.86\pm 0.82\) & \(34.55\pm 2.80\) & \(43.59\pm 1.67\) & \(43.05\pm 2.61\) & \(68.12\pm 1.43\) \\ \(P_{3-2}\) & \(56.29\pm 3.14\) & \(76.97\pm 3.31\) & \(77.40\pm 2.64\) & \(66.20\pm 3.72\) & \(60.35\pm 1.97\) & \(82.61\pm 3.31\) & \(77.67\pm 1.10\) \\ Average & \(54.14\pm 1.10\) & \(62.63\pm 1.79\) & \(63.66\pm 2.17\) & \(67.35\pm 1.97\) & \(69.42\pm 1.82\) & \(74.19\pm 1.58\) & \(\mathbf{82.16\pm 1.47}\) \\ \hline \end{tabular}
\end{table} 
Table 6: Diagnostic accuracy of PU dataset. Figure 7: Accelerated life test rig from PU. In order to further verify the transfer performance of the designed method, t-distributed stochastic neighbor embedding (t-SNE) [32] is used to map the learned high-dimensional features to the two-dimensional space to effectively demonstrate the advantages of the MPGCTN. The t-SNE diagrams generated for the task \(P_{0-3}\) using the six models are shown in Figure 8. It shows the feature distributions learned by MMD, MKMMD, CORAL, DANN, ADDA, and the MPGCTN after training. From the visualization results of the six models, it can be seen that, although the classification visualization results of the MKMMD and CORAL distinguish most categories, there are still some classification errors that lead to overlap. The experimental results show that, compared with MKMMD and CORAL, the proposed MPGCTN can capture more domain-sharing discriminant features. The visualization results show that the ADDA based on adversarial strategy has a better classification effect, second only to the proposed MPGCTN.