## 4 Discussion  
To discuss in detail the above results regarding accuracy and training time, Figures 9 and 10 have been developed. In these Figures accuracy an training time are compared for cases A to E, where the learning rate is progressively reduced. Regarding the first graph, the accuracy starts at a low level, specifically below 50%, when starting with a learning rate of 0.01. As the learning rate is reduced, accuracy increases, although not significantly. However, to achieve satisfactory results, it is necessary  
Figure 10: Training time and amor wavelet. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**TRIAL** & **A** & **B** & **C** & **D** & **E** \\ \hline Inner race fault & 293 & 289 & 293 & 95 & 186 \\ Normal & 0 & 0 & 0 & 97 & 97 \\ Outer race fault & 0 & 224 & 243 & 243 & 243 \\ Misclassified & 340 & 120 & 97 & 198 & 107 \\ Accuracy & 46.29\% & 81.04\% & 84.68\% & 68.72\% & 83.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results with test data using the morse wavelet application. Figure 9: Accuracy with training data and amor wavelet. to decrease the learning rate considerably, specifically below 0.0015, as observed in test C. Regarding the training time, the behavior is like the learning rate, that is, it increases exponentially as the learning rate is reduced. Just as in the amor wavelet, Figures 11 and 12 present the results of the morse wavelet. According to the results, if high values are applied to the learning rate, low accuracy is obtained. On the other hand, if they are reduced enough, high accuracy is obtained, in the same way as with the results of the previous wavelet. However, it can be observed that the trend is not the same as in the previous case. On the other hand, regarding the training time, it is observed that it is relatively short and that, in addition, it remains constant, even with low learning rates. This aspect is also interesting if we compare it with the training time results of the previous wavelet. Likewise, to more conveniently assess the precision, the results have been represented in Figures 13 and 14. Figure 11: Accuracy with training data using the morse wavelet. Figure 12: Time with training data using the morse wavelet. As can be seen, the above results are quite clear, since as the learning rate decreases, the number of classes correctly predicted by the network increases. In fact, this can also be seen reflected in the accuracy graph. On the other hand, if the number of predictions is considered, the network identifies a greater number of faults located in the inner track. In this regard, it should be noted that as the learning rate decreases, the number of predictions for each class tends to equalize. Again, to more conveniently assess the accuracy, the same graphs have been represented for the morse wavelet results, as seen in Figures 15 and 16. As with the other wavelet transform, the results are enlightening, since as the learning rate is reduced, the number of classes correctly predicted by the network increases. However, the overall accuracy remains relatively constant, although with a slight increase as the learning rate is reduced. Figure 14: Average accuracy results with test data using the amor wavelet. Figure 13: Accuracy results with test data using the amor wavelet. In regard to the number of predictions, once again, the network detects a higher number of errors in the internal track. In addition, there is a tendency to equalize the number of correct predictions as we decrease the learning rate. On the other hand, the maximum results obtained from the tests and their general representation can also be exposed, both in the training and validation phase and in the test phase (Figure 17). It should be noted that, to conclude which of the results is better in terms of accuracy, that is, which has the highest precision, it is necessary to observe the results obtained only with the data from the test group, since these are the ones that the neural network evaluates after having been trained.