## 6 Results and Discussion  
The experiment started with six fault conditions of the gear train, as mentioned in Table 2. The vibration signals were recorded at three different speeds for each fault condition of the gear train. For 1500 RPM, no load was applied on the gearbox, whereas for 1800 and 2000 RPM, a constant load of 280 N was applied to account for the road resistances encountered by the gear train during the operating condition, as shown in Table 3. The statistical features were extracted from the vibration signature under each condition. The participating features were selected using the accuracy by class and the attribute evaluator. Three tree algorithms were selected to classify the input features and check the accuracy at each engine speed. Table 4 shows the maximum classification accuracy for 1800 RPM for the random forest; 99% is maximum accuracy. Hence, 1800 RPM was considered for further analysis. \begin{table}
\begin{tabular}{c c} \hline
**Engine Speed (RPM)** & **Loading Condition (N)** \\ \hline
1500 & 0 \\
1800 & 280 \\
2000 & 280 \\ \hline \end{tabular}
\end{table} 
Table 3: Loading conditions at different speeds. Figure 7 reveals that vibration amplitude is higher for faulty gears than for healthy gears. The increase in vibration is ascribed to the faulty conditions of the gear. Table 5 shows the classification accuracy for all the tree family classifiers: random forest, decision tree, and random tree. The selected features under all the speed conditions were classified using the tree family algorithms, and the corresponding accuracy values are presented in Table 5. In Table 5, the decision tree and random forest produced maximum classification accuracies of 98.33% and 99%, respectively, with seven features. The random tree suggests ten features for the maximum classification accuracy of 98.67%. Under each condition, 100 data samples were used for classification. The ten-fold cross-validation was used to obtain the accuracy. From these three trees, it is seen that the maximum accuracy was found as 99% for the random forest algorithm. This can be studied using the confusion matrix obtained from the random forest algorithm. Table 6 shows the confusion matrix obtained from the random forest under a speed of 1800 RPM. The matrix displays the correct and incorrect instances classified by the algorithm. Each column of the matrix determines the predicted class of the input data, whereas each row defines the actual condition of the gearbox associated with the input data. The confusion matrix's diagonal elements are the values correctly classified in the correct nodes. In the first row, among the 100 data points belonging to SHHC, 98 data points were classified as SHHC, and 2 data points were misclassified as SGWC. In the second row, data belongs to SGWC; 97 data points were correctly classified as SGWC. Two data points were misclassified as SHHC and one data point was misclassified as SGBT. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Confusion Matrix**} \\ \hline
**A** & **B** & **C** & **D** & **E** & **F** & **Classified as** \\ \hline
98 & 2 & 0 & 0 & 0 & 0 & A = SHHC \\
2 & 97 & 1 & 0 & 0 & 0 & B = SGWC \\
0 & 1 & 99 & 0 & 0 & 0 & C = SGBT \\
0 & 0 & 0 & 100 & 0 & 0 & D = HGWC \\
0 & 0 & 0 & 0 & 100 & 0 & E = HGBT \\
0 & 0 & 0 & 0 & 0 & 100 & E = HSBT \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix for random forest algorithm. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No. of Features** & **Random Forest** & **Decision Tree** & **Random Tree** \\ \hline
1 & 40.17 & 42.17 & 40.17 \\
2 & 95.50 & 95.67 & 95.33 \\
3 & 97.00 & 95.50 & 94.00 \\
4 & 96.83 & 95.00 & 96.83 \\
5 & 96.33 & 95.00 & 96.67 \\
6 & 97.50 & 97.50 & 97.33 \\
7 & 99.00 & 98.33 & 97.00 \\
8 & 99.00 & 98.33 & 98.17 \\
9 & 98.83 & 98.17 & 98.17 \\
10 & 98.67 & 98.33 & 98.67 \\
11 & 98.67 & 98.33 & 98.33 \\
12 & 98.67 & 98.33 & 98.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect of number of features studied for the speed of 1800 RPM.