# Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets
## 4 Experimental Analysis
### Fault Diagnosis Results under the Unbalanced Dataset  
The diagnostic performance of TC-CNN is discussed in the previous section based on the balanced datasets. Traditional deep learning models need to be trained with a large number of samples to ensure good performance. In practice, however, the amount of faulty data is very small, and data imbalance is a common phenomenon. Therefore, it is necessary to solve the fault diagnosis problem under unbalanced datasets effectively. In this section, normal data and fault data are mixed in different proportions. The stability of TC-CNN for fault diagnosis is further demonstrated based on the experimental results of the datasets with different proportions. The normal and faulty samples in the training set are mixed in the ratios of 2:1, 5:1, 10:1, 20:1, 30:1, and 50:1, respectively. In the test set, the ratio of normal data to  
Then, in case 6, the number of faulty training samples is only \(1/50\) of the normal training samples, and the diagnostic Accuracy and F1 score of the proposed method are 96.80% and 0.97, respectively. The results of other methods are (40.80%, 0.37), (46.30%, 0.44), (46.30%, 0.44), (46.30%, 0.44), (87.40%, 0.87), (37.20%, 0.32), and (51.80%, 0.48). When the ratio of the number of normal samples to the number of faulty samples reaches 50:1, TC-CNN still has excellent fault diagnosis ability. The diagnostic Accuracy is only 3.20% lower than in case 1, and the F1 score decreases by 0.03. On the contrary, the fault diagnosis performance of the remaining methods decreases significantly with the reduction of the fault sample size. When the fault data in the training dataset decrease as the imbalance rate increases, the model trained by these methods lacks a good ability to identify the fault data in this case. As a result, most of the faulty samples in the test dataset could not be classified correctly, resulting in low Accuracy and F1 score. When the imbalance ratio of data distribution increases, the superiority of the TC-CNN model gradually emerges. The TC-CNN model uses FFT and GST to add fault feature information, which extracts deeply into the data features and makes fault samples easier to distinguish. When the imbalance ratio is small, such as 2:1, the diagnostic performance of TC-CNN is not much improved compared to CVT+2D-CNN. The feature richness advantage possessed by TC-CNN is relatively small when fault samples are balanced. When the data imbalance ratio is increased from 5:1 to 50:1, the fault diagnosis performance of TC-CNN does not decrease significantly compared with CVT+2D-CNN. Because CVT+2D-CNN only extracts time-frequency features, TC-CNN additionally extracts time-frequency features. The features of different dimensions complement each other, enrich the fault feature information, and make the fault samples easier to identify. When the imbalance ratios are 2:1 and 5:1, respectively, the fault diagnosis performance of 1D-CNN+2D-CNN is slightly lower than that of 2D-CNN. However, as the imbalance ratio gradually increases, the fault diagnosis performance of 1D-CNN+2D-CNN is better than that of 2D-CNN, indicating that 2D-CNN is more susceptible. The reason is that, when training samples are relatively sufficient, 1D-CNN+2D-CNN may extract some redundant features leading to overfitting of the model. However, when the data volume gradually decreases, 1D-CNN+2D-CNN can extract more features that can be utilized and, therefore, has better fault diagnosis performance. The fault diagnosis capability of TC-CNN is significantly stronger than that of 1D-CNN+2D-CNN because FFT and GST can extract more fault information with fewer fault samples, thus attenuating the effect of data imbalance. The training difficulty of DBN gradually increases as the number of training samples decreases, and it cannot effectively represent fault information. In addition, the DBN model has a  
Figure 12: F1 scores of different methods for six unbalanced dataset cases.