# Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets
## 4 Experimental Analysis
### Fault Diagnosis Results under a Balanced Dataset
(5) 1D-CNN+2D-CNN has the same network structure as TC-CNN. The difference is that TC-CNN uses FFT and GST to extract features. The inputs of the two channels are frequency spectrum and time-frequency images, respectively. In the 1D-CNN+2D-CNN, the inputs of the two channels are the original signal and the 2D matrix transformed into the original signal, respectively. To avoid biased results due to random splits of the training and testing datasets, \(k\)-fold cross-validation is applied. All samples are divided into \(k\) mutually exclusive subsets of the same size, \(k-1\) subsets are used as training samples, and the remaining subset is used as testing samples. The training samples are divided into the training set and validation set. A total of \(k\) experiments are performed to obtain \(k\) Accuracy and F1 score results, and the average is taken as the final experimental result. This paper set \(k=10\), the sample set is divided into 10 subsets, and the ratio of training set, validation set, and test set is set to: 7:2:1. The Accuracy and F1 score on balanced data samples are shown in Tables 3 and 4 and Figure 10.  
According to the results, it can be found that the methods with convolutional and pooling layers are more capable of fault diagnosis, and the DBN obtains the lowest values of Accuracy and F1 score among these models. Compared with the single-channel CNN model, the fault diagnosis performance of TC-CNN is improved due to information richness. Comparing 1D-CNN with 2D-CNN and CWT+2 DCNN shows that 2D-CNN is generally better than 1D-CNN for fault diagnosis. 1D-CNN+2D-CNN also utilizes a two-channel model. Compared with single-channel CNN, its fault diagnosis effect is better than 1D-CNN but worse than 2D-CNN. The reason is that the inputs of both channels are original signals. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Label**} & \multicolumn{6}{c}{**Method**} \\ \cline{2-7}  & **TC-CNN** & **1D-CNN** & **2D-CNN** & **CWT+2 DCNN** & **DBN** & **1D-CNN+2D-CNN** \\ \hline \(f_{1}\) & 1.00 & 0.74 & 0.86 & 0.94 & 0.58 & 0.73 \\ \(f_{2}\) & 1.00 & 0.70 & 0.77 & 0.96 & 0.71 & 0.75 \\ \(f_{3}\) & 1.00 & 0.87 & 1.00 & 0.90 & 1.00 & 1.00 \\ \(f_{4}\) & 1.00 & 0.81 & 1.00 & 1.00 & 0.58 & 0.87 \\ \(f_{5}\) & 0.98 & 0.71 & 0.92 & 0.98 & 0.51 & 0.88 \\ \(f_{6}\) & 1.00 & 0.81 & 1.00 & 1.00 & 0.72 & 0.89 \\ \(f_{7}\) & 1.00 & 0.96 & 1.00 & 1.00 & 0.74 & 0.97 \\ \(f_{8}\) & 1.00 & 1.00 & 1.00 & 0.90 & 1.00 & 1.00 \\ \(f_{9}\) & 1.00 & 0.81 & 0.95 & 1.00 & 0.52 & 0.83 \\ \(f_{0}\) & 1.00 & 0.98 & 0.97 & 1.00 & 0.96 & 1.00 \\ F1 score (macro) & 1.00 & 0.83 & 0.94 & 0.97 & 0.73 & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparison of F1 score. Figure 10: Comparison of test set fault diagnosis results. When the data set is balanced, some extracted features are redundant or unimportant, leading to over-fitting of the model. The diagnostic results of the above-mentioned various models with balanced data sets verify the superiority of TC-CNN.