## 3 Proposed Method
### The Fault Diagnosis Model of the Proposed Method  
The bearing fault diagnosis method based on the improved transfer component analysis and deep belief network mainly consists of three parts: feature extraction, data processing, and fault classification. The proposed method has two core points. One is to use ITCA to reduce the data distribution discrepancies of the same fault type between the source domain and the target domain, aligning the subdomain data distribution of the two domains. Another approach is to use K-means clustering to remove interclass edge samples and correct the pseudo labels.  
Firstly, wavelet packets are used to denoise and extract the features from the source and target the domain signals. We then use the source domain samples as the training set and divide the target domain samples into a validation set and a testing set. Then, we use a classifier trained in the source domain to label the unlabeled validation sets with pseudo labels. Then, we use K-means clustering to classify the validation set and calculate the distribution of the pseudo labels for each cluster sample. Adopting the idea of the minority obeying the majority, the pseudo labels of the samples in the top \(d\%\) of the distance from the cluster center are corrected to the one with the most pseudo labels in that cluster. Then, we perform ITCA processing on the training and validation sets and output the feature vectors after dimensionality reduction. Then, we input the training set samples after dimensionality reduction into the DBN for pretraining, and use the validation set samples after dimensionality reduction for fine-tuning the DBN. Finally, the optimal mapping relationship is applied to the test set samples, and the trained DBN model is used to classify the faults in the test set after dimensionality reduction. The specific implementation steps of the fault diagnosis method proposed are shown below, and the fault diagnosis flow chart of the proposed method is shown in Figure 5.  
Figure 4: Illustration of the SRPLC-K-means methods.  
Step 1: Denoising and feature extraction. We performed wavelet packet decomposition, denoising, and reconstruction on the bearing data. Then, we extracted multiple time-frequency features to form a feature vector;  
Step 2: Allocation of the datasets. All the source domain samples were used as the training set, a small number of unlabeled target domain samples were randomly grabbed as the validation set, and a portion of the target domain samples were taken as the testing set;  
Figure 5: The fault diagnosis flow chart of the proposed method.  
Step 3: Marking the pseudo labels. We used the DBN trained on the training set to mark the pseudo labels to the validation set;  
Step 4: Sample filtering and pseudo label correction. We filtered the edge samples and corrected the pseudo labels on the validation set using K-means clustering;  
Step 5: We performed ITCA mapping on the training and validation set samples;  
Step 6: We trained a new DBN using the training and validation sets after dimensionality reduction;  
Step 7: Using the mapping function obtained in step 5, we processed the test set samples;  
Step 8: We input the testing set samples after dimensionality reduction into the DBN trained in step 6 and output the diagnostic results.