## 4 Discussion  
This paper has trained various ML algorithms and a CNN to classify healthy and faulty REBs. Each model's best results are summarised in Figure 12. The different models have been trained using the CWRU bearing data center signals. These models are prepared to receive features of never-before-seen REBs' signals to diagnose their state of health, with an accuracy corresponding to the validation result obtained for each model. Considering the results obtained, the following can be stated:  
1. Although the frequency-domain features are ranked better, according to the applied statistical methods, than the time-domain features, it can be observed in the accuracy development of some models that some frequency-domain features contribute to worse signal classification against some time-domain features. 2. The logarithmic ratio between the maximum amplitude of the impulse responses spaced at the BPFI and the maximum amplitude of the impulse responses spaced at the BPFO is the feature containing the most relevant information about REBs' health status. That follows from the high accuracy obtained by computing the algorithms with this feature alone. According to the statistical methods, it is also the best-positioned feature in the rankings. 3. The combination of time- and frequency-domain features yields better results than by being computed separately. This can be deduced since the best results combine both types of features. To design a robust ML model that generalises correctly, the most important thing to find is the features' combination providing the most relevant information about the REBs' health status. Table 13 compares the results of some relevant papers cited in the introduction with those obtained in this paper. The Root Mean Square Error (RMSE) of the model proposed by [PERSON] et al. suggests an almost perfect REB degradation prediction compared to the data obtained in their experiment. The results of ML models trained with features based on AE analysis with the HHT, proposed by [PERSON] et al., demonstrate the usefulness of these physical features. The observer-based non-linear models of [PERSON] et al. have helped improve the accuracy of the DT model. However, these authors divide the CWRU dataset into numerous groups, considering fault sizes and different load regimes. The latter work overlooks the fact that the load is practically meaningless in this case, as there is no mechanism in the CWRU experiment that converts the torque into a radial load supported by the REBs. Furthermore, the usefulness of these non-physical models with more heterogeneous groups is unclear. [PERSON] et al. obtained excellent results in their novel neural network enhanced with the second-generation wavelet transform. [PERSON] et al. show that the simple method of converting raw \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Case A** & **Case B** \\ \hline Validation accuracy & 0.7761 & 0.9067 \\ Elapsed time & 5 min 14 s & 5 min 44 s \\ Epochs & 15 & 15 \\ Maximum iterations & 5040 & 3765 \\ Iterations per epoch & 336 & 253 \\ Frequency & 50 iterations & 50 iterations \\ Learning rate & 0.01 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: CNN results for cases A and B.  
time signals into greyscale images to train a CNN provides outstanding results. Once again, the CWRU dataset is subdivided into numerous groups in this work, considering different fault sizes and load regimes. Finally, the model proposed by [PERSON] et al. demonstrates the usefulness of DSWI obtained from faulty REBs' signals to train CNNs.