## 4 Experiment Verification
### Experimental Results
#### 4.2.1 Comparison without Transfer with Individual Models  
This study constructs seven different models including DTLCL, CNN, LSTM, AE, KNN, SVM and MMBT_mmb [46], which is the Classic SOTA for classification. Table 3 displays each model's parameters. Both DTLCL and DNN are deep network structure models, which are composed of CNN and LSTM. Their structures and parameters are the same. The difference is that DTLCL first trains the \(DNN_{s}\) model with many significant fault samples, then transfers the trained model \(DNN_{s}\) to the micro-fault diagnosis model \(DNN_{t}\), and finally trains the \(DNN_{t}\) model with a few micro-fault samples. A deep neural network model called DNN was trained using only a few micro-fault samples. The training iteration setting is 1000. The loss in CNN is cross-entropy. The AE encoding layer structure  
Figure 7: Part of fault signals collected. is 2048-128-64-6, and the decoding structure is just the opposite. All points in each domain of KNN have equal weights. The penalty parameter c in SVM is 1.1, its kernel is Gaussian kernel, and the degree parameter is 3. The dynamic learning rate (LR) is set as 0.01-0.0001 according to the epoch parameter during the training, LR is 0.01-0.0001. After a certain number of rounds, LR is gradually reduced. Near the completion of training, the learning rate declines by more than 100 times. In terms of Transfer Learning, since the model has converged on the original data set, the learning rate needs to be set as 0.0001. The hyper-parameters can strongly influence the results. The parameter settings are usually divided into grid search, manual search and random search. This study proposes an improved method of grid search called step heap sorting. First, we set the initial and maximum values of the network parameters. Second, a fixed step is given to determine the next parameter to calculate the corresponding result of the parameter. Then the theoretical results are determined using heap sorting. Finally, the results are automatically compared, and the optimal parameters are determined, as mentioned in my other public paper. To further verify that DTLCL combining CNN and LSTM produces better results than a single model-based transmission method, there is a simultaneous ablation experiment. The DTLCL model without LSTM is referred to as DTLCL_A based on the CNN-based transmission model without the LSTM structure, and the DTLCL model without CNN is referred to as DTLCL_B based on the LSTM-based transmission model without the CNN structure. In Dataset A, the error size of 0.021 inches is large and the features are significant. In dataset B, the error with a magnitude of 0.0036 inches is small and the features are not obvious. The error with a magnitude of 0.021 inches is the source region and the error with a magnitude of 0.0036 inches is the target region in this experiment. This study aims to improve the diagnostic accuracy of errors with a size of 0.0036 inches by transfer learning. To test the carryover effect from significant errors to smaller errors, two experiments are conducted in this section. Table 4 shows the conditions for significant and minor errors in Test 1 and Test 2. There are four types of significant errors, including N, IR, OR and RF. There are six types of minor errors, including N, IR, OR, RF, GPF, GBTF. The training dataset, verification dataset and significant error test dataset are divided into 1400, 400 and 100, respectively. The number of training datasets of micro-errors in Test 1 and Test 2 are different and are 50 and 200, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Working Condition** & **Significant Fault** & **Minor Fault** \\ \hline Load (hp) & 1 & 2 \\ Speed (rpm) & 1750 & 1000 \\ Fault size (inch) & 0.021 & 0.0036 \\ Status type & 4 (NRF, IF, OF) & 6 (N, RF, IF, OF, GPF, GBTF) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Operating conditions of significant and minor faults in Test 1 and Test 2.