## 4 Results
In particular, features such as VIBRATION_VEL_Vertical (mm/s), VIBRATION_VEL_Horizontal (mm/s), and ultrasound Level (dB) exhibited the highest mean absolute SHAP values, indicating their consistent and dominant influence on the model's decision-making process. Furthermore, the distribution of SHAP values across multiple classes suggests that certain features play more prominent roles in specific fault types, highlighting the class-dependent relevance of input variables. For example, VIBRATION_VEL_Vertical (mm/s) and VIBRATION_VEL_Horizontal (mm/s) were especially influential in the prediction of Class 0, Class 2, and Class 5, whereas their contribution was relatively lower for Class 6 and Class 7. In contrast, features such as KURTOIS_Horizontal, \begin{table}
\begin{tabular}{l l} \hline
**Parameters** & **Best Value** \\ \hline n\_estimators & 1000 \\ max\_depth & 10 \\ min\_samples\_split & 2 \\ min\_samples\_leaf & 1 \\ max\_features & sqrt \\ bootstrap & True \\ criterion & Gini \\ class\_weight & None \\ max\_samples & None \\ ccp\_alpha & 0 \\ max\_leaf\_nodes & None \\ \hline \end{tabular}
\end{table} 
Table 5: Parameters and best values of RF classifier algorithms with hyperparameter optimization. KUTROSIS_Vertical, and rotation speed had minimal influence, suggesting that they play a lesser role in the classification decisions. These findings highlight the importance of domain-specific features such as deflection speed and the effective capture of fault patterns. The SHAP analysis also reveals that some features are consistently informative across fault types, while others contribute minimally and can often be excluded. This supports the potential for dimensionality reduction and simplified models that are efficient enough for real-time applications without sacrificing accuracy. Moreover, the dominance of vibration velocity and ultrasound levels in feature importance as depicted in Figure 7 aligns with mechanical fault mechanisms: velocity signals reflect rotor imbalance (low-frequency faults), while ultrasound spikes indicate early bearing wear [2; 35]. The superiority of the Random Forest stems from its inherent noise tolerance, whereas AdaBoost's sensitivity to label noise explains its lower accuracy. Notably, the real-world accuracy (76.8%) obtained in this study is lower than lab-based studies [9], underscoring the challenges of industrial data variability. Figure 8 shows both training and validation accuracy scores of the Random Forest classifier that can generalize relatively better against unseen data. The validation set is a subset of the data that is separate from the training and test sets, used during the training process to tune hyperparameters and assess the model's ability to generalize to unseen data. Validation accuracy is used to monitor the model's performance during training and help prevent overfitting, ensuring that the model does not memorize the training data but instead learns to generalize well to new, unseen data. The training and validation scores  
Figure 7: Mean absolute SHAP values for the optimized RF model. were approximately similar, with no overfitting or underfitting. This indicates a well-tuned hyperparameter setting. The accuracy has leveled off to a relatively higher score, which means that the model has learned considerably well from the dataset, and also the data has been enough for the classification problem. It also certifies that the Random Forest can be considered a good candidate for fault detection and prediction on multi-sensor data, while the minor difference between the scores further strengthens the robustness of the model. Future work could focus on upscaling certain metrics through cross-validation or other ensemble techniques to improve performance. The final dataset used in this study consists of 1631 labeled samples, obtained after thorough data cleaning and pre-processing, including removing missing entries.