## 2 Related Works  
Feature extraction is a fundamental step in ML, as it transforms raw signal data into structured information that allows algorithms to detect underlying patterns, ultimately improving accuracy, computational efficiency, and model complexity [24]. When processing signal data, feature extraction spans multiple domains--time, frequency, and time-frequency--each providing unique insights [7]. In [25], nine TD features (Std, Peak, Skewness, Kurtosis, CF, RMS, Clearance Factor (CIF), SF, and IF) were extracted to diagnose faults in bearings. A methodology aimed at enhancing fault diagnosis and prediction in bearings was outlined by [26], focusing on the extraction of 10 TD features (Kurtosis, Skewness, CIF, CF, IF, SF, Peak Factor, RMS, and Std) from vibration signals combined with the application of data fusion techniques. In [27], nine TD features, including Max, Min, Mean, Std, RMS, Skewness, Kurtosis, CF, and Form factor, were extracted from vibration data from the CWRU bearing dataset at a frequency of 48 kHz to capture essential statistical properties for fault classification. Various neural network architectures, including CNN, LSTM, RNN, and Gated Recurrent Unit (GRU), were employed for 10-class classification. Ref. [28] applied feature extraction to identify 10 meaningful TD features, which are Mean, Std, RMS, Peak, SF, CF, IF, Skewness, Kurtosis, and CIF, to identify the meaningful features associated with battery deterioration. In [29], authors developed a real-time health prognostic algorithm by extracting 39 features across multiple signal curves (for 3 curves, separately: Total Harmonic Distortion (THD), RMS, Peak, Std, Mean, Signal-to-Noise Ratio, CIF, IF, CF, Kurtosis, Skewness, SF, Signal-to-Noise (SNR), and Distortion Ratio). Feature selection serves as a vital step in highlighting the most important features for classification, facilitating the development of well-trained models that deliver superior recognition accuracy. Nevertheless, constructing an optimal classification framework is inherently challenging, as a lack of sufficient features can lead to the failure of numerous classifiers. On the other hand, the presence of redundant features can undermine classification success by impairing the model's skill in discerning valuable patterns in the dataset. This redundancy often gives rise to issues such as increased dimensionality, heightened storage requirements, extended algorithm runtime, and diminished computational efficiency. Addressing these challenges necessitates the implementation of a comprehensive and highly specialized diagnostic framework to ensure precise feature selection [7; 8; 9]. In [26], a subset of the 72 extracted features was identified using the Random Forest (RF) algorithm, while 14 dimensionality reduction techniques were employed to determine the most appropriate prognostic health indicator. [PERSON]. [28] used the Extreme Gradient Boosting (XGBoost) algorithm to identify the 15 most important features out of 30 for model training, leveraging its ability to handle large datasets and complex feature interactions. In [29], filter methods were employed to select the top 19 statistically relevant features for predictive modeling, enhancing training efficiency. [PERSON]. [30] prioritized key features using FS and Mahalanobisdistance (MD), with MD being particularly useful for handling correlated features and detecting outliers. Meanwhile, [PERSON]. [31] used the Least Absolute Shrinkage and Selection Operator (LASSO) to rank 46 features, simplifying the model by shrinking irrelevant feature coefficients to 0. In [32], Sequential Feature Selection (SFS), a wrapper method, was used to identify the 10 most important features related to battery degradation, by iteratively evaluating feature combinations based on model performance. The Pearson Correlation was employed to assess the linear relationships between features and degradation, while High Gradient Boosting algorithms were used to capture complex, non-linear interactions.