## 4 Experiment Verification
### Experimental Results
#### 4.2.1 Comparison without Transfer with Individual Models
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hyper-Parameters** & **Iterations** \\ \hline CNN & structure: (2048, 1) (128, 32) (64, 32) (2048) (100) (6) & 1000 \\ LSTM & structure: (2048, 1) (2048, 32) (65,536) (32) (32) (6) & 1000 \\ AE & encoder: 2048-128-32-6; decoder \& 6-32-128-2048 & 1000 \\ KNN & n\_neighbors: 5; \(p=1\); weight: uniform; leaf\_size: 30 & / \\ SVM & Cache size: 200, degree: 3 C:1.1 & / \\ DTLCL & structure: (2048, 1) (128, 16) (64, 16) (64, 32) (32, 32) (32, 32) & \\  & (16, 32) (16, 32) (512) (512) (32) (6) & 1000 \\ DTLCL\_A & structure: (2048, 1) (128, 16) (64, 16) (64, 32) (32, 32) (32, 32) & 1000 \\  & (16, 32) (512) (512) (32) (6) & 1000 \\ DTLCL\_B & structure: (2048, 1)-(16, 32)-512-512-32-32-6 & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The hyper-parameters for models. In Test 1, 2000 training samples are required for each severe fault, and 50 training samples are required for each minor fault. In Test 2, for each major defect, there are 2000 training samples, and there are 200 training samples for each minor fault. The results are shown in the table below. First, Table 5 displays the nine models' average training, validation, testing accuracy, and training time for Tests 1 and 2 for ten trials. It can be seen that DTLCL has the highest accuracy, 0.884, 0.876, 0.885 in Test 1 and 0.953, 0.946, 0.945 in Test 2, respectively, in the training, validation and test sets. The maximum average accuracy rates of the other models without transfer are 0.786, 0.731, 0.723 in test 1 and 0.886, 0.872, 0.877 in test 2. Moreover, the standard deviations of DTLCL in test 1 are 0.52, 0.43, 0.55 and the loss rates are 0.42, 0.43, 0.43. The standard deviations of DTLCL in test 2 are 0.32, 0.33, 0.34 and the loss rates are 0.38, 0.36, 0.37. The other models without transfer's smallest variances are 0.63, 0.72, 0.64 in Test 1 and 0.43, 0.41, 0.43 in Test 2. The training time for DTLCL is 18 s in Test 1 and 19 s in Test 2, while the minimum training time for CNN in other models without transfer costs 22 s in Test 1 and 23 s in Test 2. First, DTLCL has the characteristics of high accuracy, short detection time and low deviation. Second, the average error diagnosis accuracy of each model in Test 2 is higher than that of the corresponding model in Test 1, which demonstrates that the efficiency of DCLCL is proportional to the number of samples. The result shows that the accuracy and robustness of fault diagnosis by the proposed DTLCL are significantly improved by transfer learning and the number of samples. The particular test accuracy of the different methods in the experiments is shown in Figure 8. Two points can be intuitively deduced from the analysis of the results in Figure 8. First, DTLCL has the highest accuracy of the test data set in each experiment, about 80% in Test 1 and more than 95% in Test 2. Second, the accuracy of the DTLCL test data set is stable in each experiment. In contrast, alternative approaches without transfer produce weak, unstable, and less reliable outcomes. The average accuracy of the test data set of each model in Test 2 is higher than that of the corresponding model in Test 1. These results also show that the DTLCL method can be more accurate and stable than the other five models