## 7 Experimental Results
In Table 7 and Figure 11, we can see that AMRXU-PMI+SVM and AMRXU-VSPMI+SVM had misclassification and overlap problems with inner and outer faults, but the proposed AMRXU-AVSPMI+SVM method reduced overlapping. Besides, the average accuracy in the state identification when the torque load was 2 hp in the proposed AMRXU-AVSPMI+SVM, AMRXU-VSPMI+SVM, and AMRXU-PMI+SVM was 97.35%, 95%, and 91.35%, respectively. Thus, the proposed AMRXU-AVSPMI+SVM improved average state identification accuracy from AMRXU-VSPMI+SVM and AMRXU-PMI+SVM by around 2.35% and 6%, respectively. Finally, Table 8 and Figure 12 show the accuracy in state identification when the torque load was 3 hp based on the proposed scheme (AMRXU-AVSPMI+SVM), on AMRXU-VSPMI+SVM, and on AMRXU-PMI+SVM. The average fault pattern identification by the proposed AMRXU-AVSPMI+SVM, by AMRXU-VSPMI+SVM, and by AMRXU-PMI+SVM is demonstrated in Table 9. In Table 9, the accuracy in fault pattern detection from the proposed scheme, compared with AMRXU-VSPMI+SVM, increased for ROLF, INRF, and OURF defects by 5%, 3.34%, and 3.66%, respectively. Moreover, the accuracy in fault pattern detection from the proposed scheme, compared with AMRXU-PMI+SVM, for ROLF, INRF, and OURF defects improved by 8.67%, 9%, and 9.99%, respectively. Moreover, the average crack size detection in ROLF, INRF, and OURF from using AMRXU-AVSPMI+SVM, AMRXU-VSPMI+SVM, and AMRXU-PMI+SVM is shown in Tables 10-12, respectively. \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-**} & \multirow{2}{*}{**AMRXU-PMI+SVM**} \\  & \multicolumn{3}{c}{**AVSPMI+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 (\%)** & 100 & 100 & 100 & 92 & 96 & 96 & 92 & 92 & 92 \\
**INRF (\%)** & 92 & 96 & 96 & 92 & 92 & 96 & 88 & 88 & 84 \\
**OURF (\%)** & 96 & 92 & 96 & 92 & 92 & 92 & 84 & 88 & 88 \\ \hline
**Average (\%)** & 97 & 97 & 98 & 94 & 95 & 96 & 91 & 92 & 91 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The accuracy of state identification (torque load: 2 hp). \begin{table}
\begin{tabular}{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-PMI+SVM**} \\  & \multicolumn{3}{c}{**AVSPMI+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 (\%)** & 100 & 96 & 100 & 92 & 96 & 96 & 92 & 88 & 92 \\
**INRF (\%)** & 96 & 96 & 96 & 92 & 92 & 88 & 84 & 88 & 80 \\
**OURF (\%)** & 96 & 96 & 96 & 92 & 88 & 92 & 84 & 88 & 84 \\ \hline
**Average (\%)** & 98 & 97 & 98 & 94 & 94 & 94 & 90 & 91 & 89 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The accuracy of state identification (torque load: 3 hp). Table 10 shows the average accuracy in roller crack size detection using AMRXU-AVSPMI+SVM, AMRXU-VSPMI+SVM, and AMRXU-PMI+SVM. In this table, accuracy in roller crack size detection based on AMRXU-AVSPMI+SVM, AMRXU-VSPMI+SVM, AMRXU-VSPMI+SVM, and AMRXU-PMI+SVM was 99.33%, 94.33%, and 90.67%. In addition, the average accuracy in inner crack size detection using AMRXU-AVSPMI+SVM, AMRXU-VSPMI+SVM, and AMRXU-PMI+SVM is illustrated in Table 11. In this table, accuracy in inner crack size detection based on the AMRXU-AVSPMI+SVM improved (compared with AMRXU-VSPMI+SVM and AMRXU-PMI+SVM) by 3.34% and 9%, respectively. Moreover, the average accuracy from outer crack size detection using AMRXU-AVSPMI+SVM, AMRXU-VSPMI+SVM, and AMRXU-PMI+SVM is illustrated in Table 12. In this table, accuracy from outer crack size detection with AMRXU-AVSPMI+SVM improved in comparison with AMRXU-VSPMI+SVM and AMRXU-PMI+SVM by 3.66% and 10%, respectively.