# Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer
## 5 Experimental Results
### CWRU Dataset
#### 5.1.1 Vibration Crack-Variant CWRU Dataset  
First, we investigate the fault identification capabilities of the SVM + FOR-ACO, the SVM + FOR-SPIO, and the SVM + FOR-PIO on four vibration crack-variant datasets provided by CWRU. For these investigations, the torque load remains fixed and the average accuracy (ACA) values are exposed in Tables 5-8.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Techniques** & \multicolumn{2}{c}{**SVM + FOR-ACO**} & \multicolumn{2}{c}{**SVM + FOR-SPIO**} & \multicolumn{2}{c}{**SVM + FOR-PIO**} \\ \hline Crack Sizes (inch) & 0.007 & 0.014 & 0.021 & 0.007 & 0.014 & 0.021 & 0.007 & 0.014 & 0.021 \\ NRM & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% & 88\% & 88\% & 88\% \\ BLF & 96\% & 96\% & 97\% & 93\% & 93\% & 94\% & 88\% & 78\% & 78\% \\ ORF & 87\% & 93\% & 95\% & 80\% & 93\% & 96\% & 75\% & 81\% & 78\% \\ IRF & 96\% & 96\% & 97\% & 95\% & 93\% & 93\% & 70\% & 70\% & 71\% \\ ACA & 94.8\% & 96.3\% & 97.3\% & 92\% & 94.8\% & 95.7\% & 80.3\% & 79.3\% & 78.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Window Characterization for Training and Testing: SHSE Lab Dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Techniques** & \multicolumn{2}{c}{**SVM + FOR-ACO**} & \multicolumn{2}{c}{**SVM + FOR-SPIO**} & \multicolumn{2}{c}{**SVM + FOR-PIO**} \\ \hline Crack Sizes (inch) & 0.007 & 0.014 & 0.021 & 0.007 & 0.014 & 0.021 & 0.007 & 0.014 & 0.021 \\ NRM & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% & 85\% & 85\% & 85\% \\ BLF & 95\% & 95\% & 96\% & 93\% & 93\% & 96\% & 79\% & 81\% & 84\% \\ ORF & 97\% & 97\% & 98\% & 95\% & 90\% & 90\% & 79\% & 75\% & 80\% \\ IRF & 96\% & 98\% & 99\% & 95\% & 94\% & 94\% & 75\% & 76\% & 79\% \\ ACA & 97\% & 97.5\% & 93.3\% & 95.8\% & 94.3\% & 95\% & 79.5\% & 79.3\% & 82\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The Accuracy of Single-Type Fault Identification When the Torque Load is 0-hp.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classes** & **Eight, (NRM, IRF, ORF, BLF, IRF-ORF, IRF-BLF,** \\  & **ORF-BLF, IRF-ORF-BLF)** \\ \hline Samples Per Classes & 800 \\ Training Samples & 2400 (300 samples per classes) \\ Testing Samples & 800 (100 samples per classes) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Window Characterization for Training and Testing: SHSE Lab Dataset.  
Based on these tables, the SVM + FOR-ACO (proposed scheme) outperforms the SVM + FOR-SPIO and SVM + FOR-PIO for fault identification in bearing. Moreover, the average accuracy of fault classification for the SVM + FOR-ACO is about 96.7%, while the SVM + SOR-SPIO and SVM + FOR-PIO show accuracy values of 94.3% and 80.3%, respectively. Besides, the SVM+FOR-ACO outperforms the SVM + FOR-SPIO, yielding, on average, the accuracy of identification improved by about 2.7%, 3%, and 1.1% for 0.007, 0.014, and 0.021-inch crack sizes, respectively. Additionally, the SVM + FOR-ACO outperforms the SVM + FOR-PIO, yielding, on average, the accuracy of identification improved by about 16.7%, 16.9%, and 15% for 0.007, 0.014, and 0.021 inch crack sizes, respectively.