# Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved Deep Residual Network
## 4 Experimental Verifications
### Discussion  
In order to evaluate the accuracy of the developed ResNet more effectively, we quote the precision and recall to evaluate the algorithm [40; 41; 42]. Precision is the probability of actually positive samples out of all the predicted positive samples. The recall is the probability of being predicted to be positive samples out of actually positive samples. Precision and recall are shown as follows.  
\[P=\frac{TP}{(TP+FP)} \tag{7}\]  
\[R=\frac{TP}{(TP+FN)} \tag{8}\]  
\[F1=\frac{2\times TP}{2\times TP+FP+FN} \tag{9}\]  
where \(P\) is precision, \(R\) is recall. \(TP\) stands for actual 1, predicted 1, predicted correctly. \(FP\) stands for actual 0, predicted 1, forecast wrong. \(FN\) stands for actual 1, predicted 0, forecast wrong. \(F1\) is the harmonic mean of precision and recall. In this study, According to Equation (7) and (8), the precision and recall are calculated only based on the experimental results of improved ResNet in Table 4. As shown in Table 4:  
In order to further show the ability of the improved ResNet algorithm to identify minor faults and the details of the fault misjudgment, we introduce the multi-classification confusion matrices [37] to conduct a detailed quantitative analysis of the fault results. The confusion matrices comprehensively reflect the diagnosis precision and the number of misjudgments of bearings under different fault grades, as well as the information of the real fault types being misjudged. The confusion matrices quantization diagram of bearing corresponding to Table 4 is shown in Figure 9.  
The confusion matrix of the improved ResNet is shown in Figure 9. The \(X\)-axis represents the predicted category of the fault, and the \(Y\)-axis represents the true label of the fault. The numbers on the main diagonal represent the accuracy of the improved ResNet algorithm for the correct diagnosis of each type of fault state.  
Depending on the confusion matrix, two among the ten health states present misclassifications. F1 was misjudged as F2, F7 misjudged as F4. By analyzing the types of fault misclassifications, it can be seen that the above misclassifications are all errors between different fault categories, which basically belong to small faults misjudged as larger faults, which is meaningful for risk prediction. The recognition accuracy of this algorithm is 100% between the normal state and the fault state. It can be seen that the comprehensive fault identification rate can reach 99.8%. Experimental show that the improved ResNet algorithm has superior identification ability and higher diagnostic accuracy for the micro-faults of rolling bearings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Precision (\%)** & **Recall (\%)** & **Observations** \\ \hline H0 & 98.36 & 100 & 600 \\ F1 & 100 & 100 & 600 \\ F2 & 100 & 100 & 600 \\ F3 & 100 & 100 & 600 \\ F4 & 100 & 100 & 600 \\ F5 & 100 & 100 & 600 \\ F6 & 100 & 100 & 600 \\ F7 & 100 & 98 & 600 \\ F8 & 100 & 100 & 600 \\ F9 & 100 & 100 & 600 \\ Average & 99.83 & 99.8 & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The diagnosis result evaluation of improved ResNet.