## 4 Discussion
As CWRU has a large number of samples and has high data identification, the overall performance of CWRU as the target domain data test is better than the Ottawa data set as the target domain test data. Tables 3 and 4 and Figure 7 show two datasets, the CWRU and Ottawa with their training time measured in hours and time reduction rate in percentage. We tried four sets of training. The first one is without transfer learning. The second one was with transfer learning, retaining the dense layer, and the classifier. The third one was with transfer learning and fine-tuning the dense layer only. The fourth and last one was with transfer learning and retaining the SoftMax plus (SP) classifier only. The graph just shows the same data in percentages. From the line chart, we use the model training time without knowledge transfer as the basis for comparison. The experimental results show that training the fully connected layer and the classifier is the most time-consuming but still faster than training from scratch. Only training the classifier is the most time-efficient, training to predict the CWRU dataset with a 34% time-saving and to predict the Ottawa dataset with a 68% time-saving. The experiments have three settings. First, only the output classifier is retrained; we reuse the pre-trained model as the feature extraction mechanism. The output layer is first removed, and the entire network is used as a fixed feature extractor for the new \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Time** & \multirow{2}{*}{**WO/TL**} & **W/TL** & **W/TL** & **W/TL** \\
**(Hour)** & & **(P9, F10, F11, SP)** & **(P9, F10, F11)** & **(SP)** \\ \hline Predict CWRU & 100\% & 84\% & 68\% & 34\% \\ Predict Ottawa & 100\% & 93\% & 74\% & 68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Training time reduction in percentage (Unit: %). Figure 6: ROC/AUC of feature transferring from Ottawa to CWRU. (**a**) Hidden the labels of BRF faults. (**b**) Hidden the labels of IR faults. (**c**) Hidden the labels of OR faults. dataset. Second, we retrain the top densely connected classifier: using the architecture of the pre-trained model, keeping the initial weights on the convolutional base, and adding higher dense and classification layers. Perform random initialization of all weights and retrain the model on the new dataset. At this stage, data augmentation is optional. Last, fine-tuning: After training the model with the new dataset, select and freeze some layers and retrain other top layers. Figure 8 shows the experimental results demonstrating the effectiveness and efficiency of knowledge transfer. In the bar chart, we use the accuracy of model predictions without knowledge transfer as a basis for comparison. From the experimental results, using CWRU to predict Ottawa obtains the average accuracy of 99.1% and 89.4% for known and unknown classes, respectively. The average accuracy of known and unknown classes is 99.2% and 98.2% by using Ottawa to predict CWRU, respectively. The experimental results show three points: First, the TPCCNN method inherits the advantages of the original PCCNN in distinguishing known and unknown categories with high accuracy. Second, the difference in prediction accuracy between the models with and without knowledge transfer is almost less than 1.5%. Last, using CWRU feature extraction to train and predict Ottawa's model is even more accurate than using Ottawa's model trained from scratch. Figure 8: The experimental results demonstrate the effectiveness and efficiency of knowledge transfer. Figure 7: The result of training time reduction using feature transferring from Ottawa to CWRU.