## 3 Results  
In the task of optimizing fault diagnosis in rotating machine bearings by applying deep learning technology, various experiments have been carried out in the training process in which two types of wavelets have been used: the \"amor\" (analytical morlet Gabor wavelet) and the \"morse\" (generalized morse wavelet). The distinction between \"amor\" and \"morse\" wavelets primarily lies in their mathematical formulation and flexibility, which impacts their application to data analysis. The amor (analytic Morlet) wavelet is a commonly used wavelet in continuous wavelet transforms (CWTs) due to its close resemblance to the Fourier transform, making it well suited for time-frequency analysis of signals with well-defined oscillatory components. However, it lacks full adaptability in terms of shape parameters. The morse wavelet, on the other hand, provides greater flexibility because it is defined by two parameters (symmetry and time-decay), allowing it to adapt better to different signal characteristics. This adaptability makes morse wavelets more useful in analyzing a broader range of signals, especially in cases where the amor wavelet may not be optimal. In terms of practical application, the amor wavelet is often preferred for general-purpose time-frequency analysis due to its simplicity and frequency localization properties. The morse wavelet can be tuned to different types of signals, potentially leading to better resolution and interpretability in certain cases, such as non-stationary or highly transient signals. Likewise, these parameters have been studied independently, and with the same values, in correlation with the learning rate to find out how the neural network behaves. Inthis sense, it started with an initially high learning rate and has been reduced exponentially. These tests have been carried out to examine the impact of the wavelets and the variation in the learning rate on the performance of this transfer learning neural network model. Firstly, the results obtained in the training and validation process regarding the amor wavelet in the neural network have been compiled in Table 2.  
If the wavelet transform is replaced by morse and configured with the same learning rate values, the results of the training and validation of the neural network are shown in Table 3.  
Next, the results obtained during the testing process in the neural network are analyzed, in which both wavelet transforms have been used in correlation with the confusion matrix. It is also worth mentioning that the following tables indicate, on the one hand, the values correctly predicted by the network for the three identified classes (inner race fault, normal and outer race fault) and, on the other hand, the values of the incorrectly predicted classes (misclassified). Thus, with respect to those obtained with the amor wavelet, the results of the confusion matrices for different learning rates can be verified in Table 4.  
Replacing the amor wavelet transform with the morse wavelet and applying the same learning rate values again, the results in the test phase are presented in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline
**Trial** & **A** & **B** & **C** & **D** & **E** \\ \hline Inner race fault & 293 & 293 & 293 & 274 & 158 \\ Normal & 0 & 0 & 0 & 97 & 229 \\ Outer race fault & 0 & 243 & 243 & 243 & 242 \\ Misclassified & 340 & 97 & 97 & 19 & 4 \\ Accuracy & 46.29\% & 84.68\% & 84.68\% & 97.00\% & 99.37\% \\ \hline \end{tabular}
\end{table} 
Table 4: Results with test data using the amor wavelet application. \begin{table}
\begin{tabular}{c c c c c} \hline
**Trial** & **A** & **B** & **C** & **D** & **E** \\ \hline Learning rate (1/bit) & 0.01 & 0.005 & 0.0015 & 0.001 & 0.0001 \\ Training time (s) & 28 & 454 & 483 & 437 & 2660 \\ Iterations per epoch & 63 & 63 & 63 & 63 & 193 \\ Maximum iterations & 252 & 252 & 252 & 252 & 772 \\ Validation accuracy & 42.14\% & 42.14\% & 42.14\% & 99.37\% & 98.96\% \\ \hline \end{tabular}
\end{table} 
Table 2: Results with the training data with the application of the amor wavelet.