## 1 Introduction  
Machine Learning (ML) algorithms utilize sensory data acquired throughout the production process as features to identify errors and predict failures before they occur. By implementing ML, reactive maintenance can be replaced by predictive or contingent maintenance, which is more efficient in reducing unnecessary costs and ensuring equipment reliability. However, current diagnostic and monitoring methods are typically task-specific, highlighting the need for advanced techniques to develop a more comprehensive framework to deliver satisfactory performance across diverse applications [1]. In general, the diagnosis and monitoring of industrial systems relies on two main approaches: model-based and data-driven methods. Model-based techniques for fault monitoring use mathematical models based on physical principles to predict system behaviors, making them most effective when system dynamics are well understood. Faults can be diagnosed by monitoring discrepancies between the predicted and actual values [2]. Recently, data-driven techniques have garnered considerable attention, driven by the large volumes of data produced by sensor systems throughout industrial processes [3; 4]. Unlike model-based methods, which require detailed system modeling and fatigue life calculations, data-driven techniques can extract hidden patterns from large datasets without domain-specific knowledge, making them highly adaptable and scalable. Typically, sensory signals collected experimentally undergo feature extraction to interpret the raw data. Once features are extracted, traditional classification and regression techniques are applied to make predictions on a case-by-case basis [5]. Feature selection serves a vital function in identifying the most significant features for classification and regression tasks, improving algorithm efficiency during the training phase and model interpretability and generalization, thereby reducing overfitting [6; 7]. This process enhances computational efficiency by reducing memory usage and training time, particularly for large datasets, while also making models more interpretable. The presence of redundant features can increase dimensionality, require excessive storage, extend algorithm runtime, and ultimately reduce overall efficiency [8; 9]. To mitigate these challenges, selecting the appropriate feature size is essential for developing effective custom diagnostics [10]. In addition, employing cross-validation ensures the robustness of selected features across different data subsets. Depending on the dataset and model, filter, wrapper, or embedded methods can be used to optimize feature selection. This study proposes a pipeline to enhance classification success by extracting time-domain (TD) features from raw data, such as Minimum (Min), Maximum (Max), Absolute Max (AbsMax), Mean, Standard Deviation (Std), Root Mean Square (RMS), Skewness, Kurtosis, Variance (Var), Peak-to-Peak (P2P), Impulse Factor (IF), Crest Factor (CF), Shape Factor (SF), Mobility (Hjorth Parameters), and Complexity (Hjorth Parameters) [11; 12; 13; 14; 15]. Feature Selection Methods (FSM) such as the Fisher Score (FS), Mutual Information (MI), Sequential Feature Selection (SFS), Recursive Feature Elimination (RFE), and Random Forest Importance (RFI) are employed to highlight the most critical features, each contributing to noise reduction and dimensionality optimization. The classification results are analyzed through accuracy, precision, recall, and F1-score, utilizing the SVM algorithm from traditional ML methods and Long Short-Term Memory (LSTM) algorithm from Deep Learning (DL) methods [16; 17; 18; 19; 20; 21]. The performance of the proposed fault diagnostic model is validated on the CWRU bearing dataset [22] and the NASA PCoE lithium-ion battery dataset [23]. This research is primarily concerned with comparing various FSMs on datasets drawn from different domains, utilizing both ML and DL techniques.