## 5 Results and Discussion
### Fault Events Classification
The general architecture and theory of DBN are presented in Section 4.8. In this case, we used all the features for the training of DBN. Similar five-fold cross-validation was performed to analyze the output results. The output confusion matrix corresponding to the DBN classifier is shown in Figure 10. As shown in Figure 10, the overall accuracy of the classifier was found to be 98.9% with 96.1% dependability or TPR for Class-1. Although the classifier performed better compared with other ML-based DMTs to classify the fault, the average testing time was slightly higher (refer to Table 10). Moreover, Table 10 depicts the performance of each DMT in noisy conditions. Here, we have noted a similar kind of observation as presented for without noisy data, but with a reduced accuracy level. \begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**No. of Features**} & \multicolumn{1}{c}{**Selected Features**} & \multicolumn{1}{c}{**Accuracy (\%)**} \\ \hline
1 & F3 & 64.72 \\ \hline
2 & F3,F12 & 89.33 \\ \hline
3 & F3,F12,F6 & 94.45 \\ \hline
4 & F3,F12,F6,F4 & 97.72 \\ \hline
5 & F3,F12,F6,F4,F5 & 97.83 \\ \hline
6 & F3,F12,F6,F4,F5,F10 & 97.8 \\ \hline
7 & F3,F12,F6,F4,F5,F10,F13 & 98.42 \\ \hline
8 & F3,F12,F6,F4,F5,F10,F13,F14 & 98.62 \\ \hline
9 & F3,F12,F6,F4,F5,F10,F13,F14,F1 & 98.73 \\ \hline
**10** & **F3,F12,F6,F4,F5,F10,F13,F14,F1,F1,F2 & **98.82** \\ \hline
11 & F3,F12,F6,F4,F5,F10,F13,F14,F1 & 98.81 \\ \hline
12 & F3,F12,F6,F4,F5,F10,F13,F14,F1 & 98.78 \\ \hline
13 & F3,F12,F6,F4,F5,F10,F13,F14,F1 & 98.78 \\ \hline
14 & F3,F12,F6,F4,F5,F10,F13,F14,F1 & 98.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance of fault detection using SFS with KNN (five-fold cross-validation). Figure 5: Confusion matrix corresponding to the highest accuracy as shown in Table 5: (**a**) confusion matrix showing numerical values (**b**) TPR and FNR (in percentages). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data-Mining Methods** & **Selected Features** & \
\begin{tabular}{c} **Accuracy** \\ **(No Noise)** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **(20 dB Noise)** \\ \end{tabular} & **Avg. Testing Time** \\ \hline ANN & F11,F14,F3,F7,F8,F5,F12,F10,F6,F13,F9,F1 & 91.5\% & 82.5\% & 8.05 ms \\ \hline SVM & F3,F2,F8,F12,F6,F1,F10,F5,F11,F4,F7,F13,F14,F9 & 97.3 & 88.6\% & 6.03 ms \\ \hline ELM & F3,F8,F11,F13,F6,F10,F2,F14,F5,F9,F12,F1 & 84\% & 81.3\% & 1.59 ms \\ \hline RF & F4,F3,F1,F2,F7,F5,F6,F8,F9,F10,F11,F12,F13,F14 & 96.7\% & 88.7\% & 0.58 ms \\ \hline KNN & F3,F12,F6,F4,F5,F10,F13,F14,F1,F2 & 98.8\% & 92.0\% & 0.89 ms \\ \hline DBN & F1,F2,F3,F4,F5,F6,F7,F8,F9,F10,F11,F12,F13,F14 & 98.9\% & 91.8\% & 2.59 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance of data-mining techniques in fault detection with noisy conditions. Figure 6: Confusion matrix corresponding to the highest accuracy shown in Table 6: (**a**) confusion matrix showing numerical values (**b**) TPR and FNR (in percentages). Figure 7: Confusion matrix corresponding to the highest accuracy shown in Table 7: (**a**) confusion matrix showing numerical values (**b**) TPR and FNR (in percentages).