## 5 Discussion  
Case 1: This study demonstrates that utilizing 10 features (Mobility, Standard, Variance, Mean, RMS, Complexity, P2P, Kurtosis, Max, and AbsMax) selected by RFI is sufficient to attain 98.19% and 98.41% F1-scores in the CWRU bearing dataset for the SVM and LSTM models, respectively, as indicated in Table 8 and Figure 4. Using all features, 98.39% accuracy and 98.32% F1-scores were obtained with the LSTM model. It was observed that the performance increased after feature selection. Ref. [4] utilized 2800 samples distributed across 10 classes, analyzed using 5-fold cross-validation, whereas our research employed 2300 samples obtained from 230 windows across the same 10 classes, using a 10-fold cross-validation approach. This study also extracted 14 unique TD and statistical features, achieving 97% accuracy with the RF method and 95.27% accuracy when utilizing raw data with CNN. After incorporating a filter and a sliding window technique, the CNN demonstrated substantial performance improvement, achieving an accuracy rate of 98.50%. Furthermore, the sensitivity, recall, and F1-scores of the model were recorded as 98.60%, 98.57%, and 98.57%, respectively. In ref. [27], Max, Min, Mean, Standard, RMS, Skewness, Kurtosis, Crest Factor, and Shape Factor features are extracted from the 48 kHz COWRU bearing dataset. Using CNN, LSTM, RNN, Gated Recurrent Unit (GRU), and Bidirectional LSTM methods, CNN and LSTM models achieved 95.00% accuracy, RNN and GRU models achieved 97.00% accuracy, and the Bidirectional LSTM model achieved 96.00% accuracy. In another study presented in the literature, 12 kHz DE, 48 kHz DE, and Fan End (FE) data of the COWRU bearing dataset were used [51]. The 48 kHz DE dataset results of this study were compared with our current study. The shaft speed rpm information of the data is not shared. The dataset includes 10 classes of data, each consisting of 50 samples. Each sample contains 2048 sample points. A total of 27-time, frequency, and entropy energy features were obtained. Using the Relief Feature Selection Method, the best 11 features were determined. These features are Kurtosis, P2P, Var, SF, Margin Factor, RMS Frequency (RMSF), Frequency Variance (VF), Root Frequency Variance (RVF), Power Spectrum H1, Power Spectrum H2, and Intrinsic Mode Function6 (IMF6) E6. Methods such as the Adaboost, SVM, GDBT, KNN, Gray Relational Degree (GRD), Bagging, CNN, and LSTM methods were employed. The performance, evaluated using the accuracy metric, is depicted in the accompanying graph. While the results are not presented explicitly as percentage values, the outcomes of the SVM and LSTM algorithms were found to be similar to those of our approach. The literature comparison is given in Table 9.  
This work aims to address the problem in a simpler and more efficient way. Our aim is to present a method that achieves high performance in the prediction process by determining an efficient feature selection process. Case 2: This study demonstrates that utilizing 10 features (Voltage Mean, Voltage RMS, Voltage IF, Voltage CF, Current Mean, Current RMS, Current IF, Current CF, Temperature, and Kurtosis) selected by RFI is sufficient to attain 96.82% and 98.80% F1-scores in the  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & **Feature Extraction and** & **Performance Metrics (\%)** \\  & **Selection Technique** & **Accuracy:** \\  & & features with RF & 95.27\% (raw data) \\  & & 98.57\% (all features selected) \\ \hline \multirow{2}{*}{[27]} & 9 TD features without & CNN and LSTM Accuracy: 95.00\% \\  & & \ \begin{tabular}{c} GRU and RNN Accuracy: 97.00\% \\ Bi-LSTM Accuracy: 98.00\% \\ \end{tabular}  \\  & & \begin{tabular}{c} 27 features obtained. \\ 11 features selected \\ with ReliefF \\ \end{tabular}