## 7 Experimental Results
Figure 3.4 The average fault identification accuracy based on (**A**) the proposed AMRXU-AVSPMI+SVM, (**B**) AMRXU-VSPMI+SVM, and (**C**) AMRXU-PMI+SVM, (torque load: 2 hp). Figure 3.5 The average fault identification accuracy based on (**A**) the proposed AMRXU-AVSPMI+SVM, (**B**) AMRXU-VSPMI+SVM, and (**C**) AMRXU-PMI+SVM (torque load: 1 hp). Figure 3.6 The average fault identification accuracy based on (**A**) the proposed AMRXU-VSPMI+SVM, (**B**) AMRXU-VSPMI+SVM, and (**C**) AMRXU-PMI+SVM (torque load: 0 hp). Table 5 and Figure 9 demonstrate the accuracy of state identification when the torque load is 0 hp based on the proposed scheme (AMRXU-AVSPMI+SVM), AMRXU-VSPMI+SVM, and AMRXU-PMI+SVM. From Figure 9, it is clear that AMRXU-VSPMI+SVM and AMRXU-PMI+SVM have high misclassification problems, especially between the inner and outer faults. The proposed AMRXU-AVSPMI+SVM reduced this challenge. Moreover, the average accuracy of state identification when the torque load was 0 hp from the proposed AMRXU-AVSPMI+SVM, AMRXU-VSPMI+SVM, and AMRXU-PMI+SVM was 98.65%, 95.67%, and 91.35%, respectively. Thus, the proposed AMRXU-AVSPMI+SVM improved the average state identification accuracy compared to AMRXU-VSPMI+SVM and AMRXU-PMI+SVM by around 2.98% and 7.3%, respectively. Table 6 and Figure 10 demonstrate the accuracy in state identification when the torque load was 1 hp based on the proposed scheme (AMRXU-AVSPMI+SVM), on AMRXU-VSPMI+SVM, and on AMRXU-PMI+SVM. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Technique**} & \multicolumn{3}{c}{**AMRXU-**} & \multicolumn{3}{c}{**AMRXU-**} & \multicolumn{3}{c}{**AMRXU-**} & \multirow{2}{*}{**AMRXU-PMI+SVM**} \\  & & **AVSPMI+SVM** & & & & & **VSPMI+SVM** & & \\ \hline
**Crack Sizes (in.)** & 0.007 & 0.014 & 0.021 & 0.007 & 0.014 & 0.021 & 0.007 & 0.014 & 0.021 \\
**NORM (\%)** & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\
**ROLF (\%)** & 96 & 100 & 100 & 92 & 92 & 96 & 88 & 92 & 88 \\
**INRF (\%)** & 92 & 96 & 96 & 92 & 92 & 92 & 92 & 84 & 88 \\
**OURF (\%)** & 100 & 96 & 96 & 96 & 92 & 92 & 80 & 88 & 92 \\ \hline
**Average (\%)** & 97 & 98 & 98 & 95 & 94 & 95 & 90 & 91 & 92 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The accuracy in state identification (torque load: 1 hp). Figure 12: The average fault identification accuracy based on (**A**) the proposed AMRXU-AVSPMI+SVM, (**B**) AMRXU-VSPMI+SVM, and (**C**) AMRXU-PMI+SVM (torque load: 3 hp). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Technique**} & \multicolumn{3}{c}{**AMRXU-**} & \multicolumn{3}{c}{**AMRXU-**} & \multirow{2}{*}{**AMRXU-**} \\  & & **AVSPMI+SVM** & & & & **VSPMI+SVM** & & & **AMRXU-PMI+SVM** \\ \hline
**Crack Sizes (in.)** & 0.007 & 0.014 & 0.021 & 0.007 & 0.014 & 0.021 & 0.007 & 0.014 & 0.021 \\
**NORM (\%)** & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\
**ROLF (\%)** & 100 & 100 & 100 & 96 & 92 & 96 & 96 & 92 & 84 \\
**INRF (\%)** & 96 & 100 & 96 & 92 & 92 & 96 & 92 & 88 & 84 \\
**OURF (\%)** & 96 & 96 & 100 & 96 & 96 & 92 & 80 & 88 & 92 \\ \hline
**Average (\%)** & 98 & 99 & 99 & 96 & 95 & 96 & 92 & 92 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The accuracy in state identification (torque load: 0 hp). Based on Figure 10 and Table 6, it is clear that AMRXU-PMI+SVM had a misclassification problem, especially between inner and outer faults, which the proposed AMRXU-AVSPMI+SVM method reduced. Furthermore, the average accuracy in state identification when the torque load was 1 hp with the proposed AMRXU-AVSPMI+SVM, with AMRXU-VSPMI+SVM, and with AMRXU-PMI+SVM was 97.7%, 94.65%, and 91%, respectively. Thus, the proposed AMRXU-AVSPMI+SVM improved average state identification accuracy compared to AMRXU-VSPMI+SVM and AMRXU-PMI+SVM by around 3.05% and 6.7%, respectively. Table 7 and Figure 11 demonstrate the accuracy in state identification when the torque load was 2 hp based on the proposed scheme (AMRXU-AVSPMI+SVM), on AMRXU-VSPMI+SVM, and on AMRXU-PMI+SVM.