## 4 Experimental Results
### Experimental Results without ABC Algorithm  
This section presents the experimental results of the cases in which the ABC algorithm was not used to select the most relevant attributes of the DMI signatures. Therefore, all DMIvalues (150 attributes) are used in the feature extraction stage as the C4.5 DT and MLP ANN classifiers' input data. As described above, several operating conditions found in industrial environments are emulated, such as variations of frequency, load and bearing failure levels. Table 1 shows the classification results for this first case performed on Motor 1. Table 1 shows that the best results are obtained using the MLP classifier independently of the supply motor's frequency inverter. Particularly for the MM440 inverter, the classification rate is 99.8% of the overall data set, 100% of the healthy samples, and 99.7% of the faulty ones. The Kappa index of 0.99 supports the results described above. This shows that the use of mutual information combined with the MLP classifier can effectively separate classes. Similarly, it is possible to observe in Table 1 that the C4.5 classifier achieves results below the MLP classifier. However, 94.3% of all samples are correctly identified,which represents a satisfactory result. However, it is noteworthy that the C4.5 classifier takes 0.19 s to build the model, which is much shorter than the MLP network, which takes 10.7 s. Therefore, the C4.5 decision tree is also presented as a promising proposal for the diagnosis of bearing failures in the TIM fed by the MM440 inverter. Analyzing the results presented for Motor 1 fed by G110 inverter, it can be seen that over 90.5% of the overall samples are properly identified. The RNA MLP to diagnose bearing failures achieves a classification accuracy of 98.1%, 93.2%, and 99.3% of the overall, healthy, and faulty samples, respectively. The Kappa index of 0.94 confirms the results obtained. As for the C4.5 classifier, it is possible to verify inferior results than the MLP ANN, where 90.5% of the samples are correctly classified. The system has greater difficulty in diagnosing healthy samples according to Table 1, only 64.4% of the samples are diagnosed. The Kappa index of 0.67 represents a significant agreement with the results obtained with this classifier. After identifying the bearing failures in Motor 1, further experimental tests are performed with Motor 2. Table 2 shows the classification results considering the four classes of bearing failure and using the two frequency inverters. According to Table 2, when the MM440 frequency inverter feeds the motor, the best separation between healthy and faulty classes is obtained using the MLP network. In this case, a classification accuracy of 99.2% of the overall samples is achieved, in addition to 100% and 99.0% of the healthy and faulty samples, respectively. When using the C4.5 classifier, lower classification rates are achieved, 94.9% of the overall samples. For this \begin{table}
\begin{tabular}{l c c c c} \hline
**Data Set** & \multicolumn{2}{c}{**MM440 Inverter**} & \multicolumn{2}{c}{**G110 Inverter**} \\ \hline Classifier & C4.5 & MLP & C4.5 & MLP \\ Global Accuracy (\%) & 94.3 & 99.8 & 90.5 & 98.1 \\ Healthy Accuracy (\%) & 85.1 & 100 & 64.4 & 93.2 \\ Faulty Accuracy (\%) & 96.6 & 99.7 & 97.0 & 99.3 \\ Kappa Index & 0.82 & 0.99 & 0.67 & 0.94 \\ Time to build the model (s) & 0.19 & 10.7 & 0.08 & 9.56 \\ \hline \end{tabular}
\end{table} 
Table 1: Experimental results using all the Delayed Mutual Information (DMI) values—0.74 kW motor. \begin{table}
\begin{tabular}{l c c c c} \hline
**Data Set** & \multicolumn{2}{c}{**MM440 Inverter**} & \multicolumn{2}{c}{**G110 Inverter**} \\ \hline Classifier & C4.5 & MLP & C4.5 & MLP \\ Global Accuracy (\%) & 94.9 & 99.2 & 92.6 & 99.7 \\ Healthy Accuracy (\%) & 86.1 & 100 & 73.7 & 100 \\ Faulty Accuracy (\%) & 97.1 & 99.0 & 97.4 & 99.7 \\ Kappa Index & 0.84 & 0.98 & 0.76 & 0.99 \\ Time to build the model (s) & 0.06 & 9.75 & 0.06 & 9.33 \\ \hline \end{tabular}
\end{table}