## 4 Experimental Results
### Experimental Results Using the ABC Algorithm  
This section presents the experimental results when meta-heuristic classifiers are used to identify failures. Similar to the tests made in Section 4.1, all data sets include variations in the power supply frequency from 20% to 100% of the nominal frequency and variations in the mechanical load torque of 10% to 120% of its nominal value. As described in Section 3, the 150 attributes of the DMI signature are evaluated individually using the CFS algorithm present in ABC, which uses meta-heuristic merit to predict classes. The attributes with the greatest merit are identified as the most relevant for fault diagnosis. Therefore, they are used as input data for the construction of the classification model. For the Motor 1 data set fed by the MM440 frequency inverter, the ABC meta-heuristic selected 23 attributes: 29, 51, 55, 60, 66, 68, 82, 84, 86, 91, 92, 96, 106, 108, 110, 113, 114, 116, 119, 120, 121, 142 and 144, which constitute the matrix of 150 attributes of the DMI signature. In the case in which the G110 inverter was used to feed Motor 1, 17 attributes were selected using the ABC algorithm, among which are 49, 52, 54, 62, 74, 75, 89, 100, 109, 110, 114, 121, 127, 133, 138, 145, 147 of the 150 DMI attributes. Table 3 presents the experimental results for the new tests of Motor 1. By operating the ABC algorithm on the attribute selection for the Motor1 data set fed by the MM440 inverter, it is observed that there was a decrease of 0.70% and 1.40% in the accuracy for the C4.5 and MLP classifiers, respectively, compared to the tests presented in Section 4.1. However, the accuracy of the classification is still satisfactory, as can be seen in Table 3. Using the MLP pattern classifier, 98.4%, 95.4%, and 99.1% of the overall, healthy, and faulty samples were correctly identified, respectively. In this case, the Kappa index of 0.95 confirms the results obtained. For the C4.5 meta-heuristic classifier, 93.6% of all samples, 81.6% of the healthy samples, and 96.6% of the faulty ones were correctly detected. The Kappa coefficient of 0.80 confirms the agreement with the results, since values equal to or higher than 0.80 indicate this relation. It is also possible to note in Table 3 that using the G110 inverter for feeding Motor 1. The accuracy rates are slightly lower compared to the tests using the 150 DMI attributes. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Data Set** & **MM440 Inverter** & \multicolumn{2}{c}{**G110 Inverter**} \\ \hline Classifier & C4.5 & MLP & C4.5 & MLP \\ Global Accuracy (\%) & 93.6 & 98.4 & 90.5 & 95.7 \\ Healthy Accuracy (\%) & 81.6 & 95.4 & 56.2 & 89.0 \\ Faulty Accuracy (\%) & 96.6 & 99.1 & 99.0 & 97.3 \\ Kappa Index & 0.80 & 0.95 & 0.65 & 0.86 \\ Time to build the model (s) & 0.13 & 1.44 & 0.06 & 1.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results using the artificial bee colony (ABC) algorithm—0.74 kW motor. The proposed methodology correctly identifies 95.7% of the total samples using the MLP meta-heuristic classifier. Even so, 89.0% and 97.3% of the healthy and faulty samples are properly diagnosed. The Kappa coefficient of 0.86 confirms these promising results. Moreover, when the C4.5 meta-heuristic classifier was used, 90.5% of the global samples are correctly classified, according to Table 3. It is possible to note that this classifier had greater difficulty in identifying healthy samples. The Kappa index represents a substantial agreement with the obtained results. Similar to the tests performed on Motor 1, the ABC meta-heuristic algorithm is used to select the relevant DMI attributes from the Motor 2 data set. These selected attributes are used as inputs to the MLP network and C4.5 classifiers. Using this technique, 20 DMI attributes were selected for the MM440 inverter data set: 1, 30, 41, 42, 56, 67, 70, 103, 109, 114, 115, 116, 117, 119, 121, 122, 125, 127, 148, 149, which constitute the matrix of 150 attributes of the DMI curve.