## 3 Materials and Methods
### Model Training and Validation  
Data training and validation are conducted with the Python programming language using scikitlearn, XGBoost, LightGBM, matplotlib, seaborn, pandas, and numpy on an Intel Core i7 device with an onboard graphics processor, 512 GB of SSD storage, and 16 GB of RAM. The MSDCP (Machine learning-based Systematic Dataset Comparison and Prediction) algorithm automates the steps of pre-processing, feature selection, model training, and performance evaluation on a given dataset, aiming to conduct a comparative analysis of different machine learning algorithms. The overall procedure of the MSDCP approach is described in detail in the pseudocode provided in Algorithm 1. The MSDCP function takes four main input parameters: \(D\) represents the raw dataset. \(A\) denotes the list of algorithms to be evaluated. \(F\) refers to the selected features or feature selection criteria, and \(P\) indicates the pre-processing settings. During the pre-processing phase, the dataset is prepared based on the specified configurations; subsequently, selected features are used to optimize the dataset. The processed data is then split into the training and testing sets, with 80% allocated for training and 20% for testing. The training data is used to build models, while the test data is reserved for evaluating model performance. In this stage, various machine learning algorithms (e.g., the SVM, the RF, etc.) are trained and tested. To enhance model accuracy, hyperparameter optimization is performed, allowing fine-tuning of the model parameters. The results are ranked based on performance and visualized. The performance outcomes for each algorithm are presented in Table 3, and the overall workflow is depicted in Figure 5. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Fault Category** & **Symptoms** & **Possible Root Causes** \\ \hline
5—Rotation Frequency \& Harmonics & Increase in vibration levels at the rotating speed or multiples (rpm, 2\(\times\) rpm, and 3\(\times\) rpm) especially on the horizontal or vertical axis. & Alignment issue, bent shaft, misaligned bearing or improper clearance, rotor unbalance, and rotating looseness. \\ \hline
6—Rotation Frequency & Increase in vibration levels at the rotation speed (rpm) on the horizontal or vertical axis. & Unbalance, eccentricity in gear rotor/stator unbalance, and mechanical looseness. \\ \hline
7- Low-Frequency Vibrations & Increase in low-frequency vibration levels on the horizontal or vertical axis & Eccentricity, soft foot, worn or loose belt, turbulence, and structural weakness. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._The heatmap visualization in Figure 6 illustrates the interconnections among numerical variables through correlation analysis. The presence of high correlation points to possible redundant features, whereas lower correlation coefficients suggest that certain variables may offer distinct information for fault classification purposes. It would be beneficial to elaborate on how these correlation patterns guide research decisions in feature selection and their subsequent effects on the model's effectiveness. The following section builds on these findings by examining the resulting classification performance across different algorithms. Figure 5: Flow diagram of the proposed system.