# A Fault Diagnosis Method of Mine Hoist Disc Brake System Based on Machine Learning
## 6 Experimental Verification
### Result Analysis  
(1) Classification accuracy test  
The authors collected 100 sets of data of various fault data to test the above diagnostic rules. The results of the tests appear in Table 9. The classification accuracy is up to 95.85%, and the model meets the classification accuracy requirements. (2) Evaluation index analysis before and after algorithm improvement  
Figure 3: K_C4.5 decision tree classification result. \begin{table}
\begin{tabular}{c l l} \hline \hline
**No** & \multicolumn{1}{c}{**Rules**} \\ \hline
1 & If brake shoe clearance \textless{}=1.07 then F1 occurrence \\
2 & If brake shoe clearance \textgreater{}1.96 and contact area \textless{}=62\% then F2 occurrence \\
3 & If brake shoe clearance \textgreater{}1.96 and contact area \textgreater{}62\% then F3 occurrence \\
4 & If 1.07\textless{} brake shoe clearance \textless{}=1.96 and hydraulic station residual pressure \textless{}=0.32 then F4 occurrence \\
5 & If 1.07\textless{} brake shoe clearance \textless{}=1.96 and hydraulic station residual pressure \textgreater{}0.51 then F5 occurrence \\
6 & If 1.07\textless{} brake shoe clearance \textless{}=1.96 and 0.32\textless{} hydraulic station residual pressure \textless{}=0.51 and brake disc swing offset \textgreater{}0.53 then F6 occurrence \\
7 & If 1.07\textless{} brake shoe clearance \textless{}=1.96 and 0.32\textless{} hydraulic station residual pressure \textless{}=0.51 and brake disc swing offset \textless{}=0.53 then F7 occurrence \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The fault data set generates rules under the K_C4.5 algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Type** & \
\begin{tabular}{c} **Number of Test** \\ **Samples** \\ \end{tabular} &
\begin{tabular}{c} **Number of Correctly** \\ **Classified Samples** \\ \end{tabular} & **Accuracy** \\ \hline Brake shoe clearance is small & 100 & 97 & 97\% \\ Brake disk overheating & 100 & 98 & 98\% \\ Cavity time is too long & 100 & 92 & 92\% \\ Emergency brake fault & 100 & 96 & 96\% \\ Residual pressure is too large & 100 & 94 & 94\% \\ Disc spring fault & 100 & 99 & 99\% \\ Normal & 100 & 100 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Diagnostic rule test results. This study uses the following evaluation indicators to test the performance of the algorithm before and after improvement: decision tree size, number of decision rules, tree building time, correct classification percentage, and difference degree (Kappa statistic). Among them, the decision tree size refers to the total number of nodes generated, the number of decision rules refers to the number of diagnosis rules finally generated, and the Kappa statistic \(K\) is used to evaluate the difference between the classification result of the classifier and the random classification. \(K=1\) indicates that the classifier is completely different from the random classification. \(K=0\) indicates that the classifier is the same as the random classification and has no classification effect. The closer the value is to 1, the better. The results of the tests appear in Table 10.  
(3) Accuracy test before and after algorithm improvement in big data samples  
To better test the accuracy change of the C4.5 algorithm before and after improvement, this study observes the change of accuracy by increasing the number of samples (taking 1000 samples). The samples are classified according to the diagnostic rules generated by K_C4.5 algorithm and the original C4.5 algorithm. The accuracy of the final test results is shown in Figure 4.  
Figure 4 shows that the accuracy of the two algorithms is similar when the number of samples is small, but with the increase of the number of samples, it can be seen that the accuracy of K_C4.5 algorithm is significantly higher than the original C4.5 algorithm, and gradually stabilizes at a higher level.