## 4 Discussion  
The contribution of this paper to the feature reduction methods are aggregated into two categories: data-level and algorithm-level approaches. The data-level approach consists of encoding time series using FFT to clean and produce de-noised input signals which offer a more efficient CNN training. In the real world, if the spectrum with heavy noise, the FFT can efficiently clean the data and retrieve smooth results we expect. In the algorithm level approach, one is the PCCNN algorithm which has a self-determined and self-learned ability to distinguish between unknown and known classes. The other is a transfer learning algorithm with adaptive convolutional layer filters and classifiers to analyze the input time series signals, including noise fluctuation. According to the previous description, CWRU and Ottawa are used for model training and evaluation. The proportions of training and test sets for each dataset are 70% and 30%. Since the sampling lengths of each data set are different, the sliding sampling method is adopted. The sliding data interval of CWRU and Ottawa is set to 128 and 768, respectively. Experimental results are visualized using the confusion matrix and AUC/ROC curve. Figures 3 and 4 show the confusion matrix and test results for transferring the extracted features from the source domain CWRU dataset to the target domain Ottawa dataset. The dataset of the target domain Ottawa has three health states: normal, faulty with an outer race defect (OR), and faulty with an inner race defect (IR). There are two sub-experiments to test the accuracy: (a) hidden the labels of inner race faults (IR) and (b) hidden the labels of outer race faults (OR). The dataset has 11,000 samples and is divided into the training set and test set according to the ratio of 7:3. Thus, there are 7700 samples in the training set and 3300 pieces of data in the test set. Figure 3: Confusion matrix of feature transferring from CWRU to Ottawa. (**a**) Hidden the labels of inner race faults (IR). (**b**) Hidden the labels of outer race faults (OR). As the confusion matrix shows, when the new fault classes are different, the model still accurately recognizes the known and unknown classes. When OR is used as an unknown category, it has an accurate judgment of the data of the known category, and the AUC value is 1.00. When IR is used as the unknown category, the judgment of the known category is also accurate, and the AUC value is also 1.00. By taking different types of faults as unknown categories, the recognition degree of the model to different categories and the robustness of the model is stable. Figures 5 and 6 show the confusion matrix and test results for transferring extracted features from the source domain Ottawa dataset to the target domain CWRU dataset. The target domain CWRU dataset has four health states: normal, outer ring bearing failure (OR), inner ring bearing failure (IR), and ball failure (BF). There are three sub-experiments to test the accuracy: (a) hidden the labels of ball faults (BRF), (b) hidden the labels of inner race faults (IR), and (c) hidden the labels of outer race faults (OR). Figure 4: ROC/AUC of feature transferring from CWRU to Ottawa. (a) Hidden the labels of inner race faults (IR). (b) Hidden the labels of outer race faults (OR). Figure 5: Confusion matrix 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. The CWRU dataset has a length of 14,667 samples and is divided into the training set and test with a ratio of 7:3. The number of the training and test set data is 10,267 and 4400, respectively. The confusion matrix shows that though the fault categories of the target domain are different, the model can still accurately recognize the known and unknown categories. The AUC values of the three different unknown categories are all 1.00, and the AUC values of the data of the known categories are also 1.00.