# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
## 4 Results
This low variability suggests that RFC6 can provide more stable performance across multiple experiments or data splits. Observing the RMS and kurtosis metrics, RFC6 also showed the highest RMS (0.985), indicating that its accuracy distribution was closer to the ideal level. When using only four features (mean, standard deviation, RMS, and kurtosis), both SVC and RFC models maintained relatively high accuracy, though this was slightly lower than their six-feature counterparts. Notably, RFC4 achieved an accuracy of 97.52%. In fact, the minimum and maximum calculations required almost no additional resources. The cost of calculation was not relevant. Rather, it was sufficient to keep only the four parameters. These sufficiently characterized a normal distribution, an assumption that was easily verified; consequently, these parameters were deemed sufficiently contributive, as seen in the example (see Figure 5). To illustrate the model's capability, we analyzed the 'SVC6 2400, 2 HP, 99.65% accuracy' case in detail. This example was selected due to its exceptional performance in the high-load scenario (2 HP), fault detection in this kind of scenario may be critical for preventing catastrophic failures. The subsequent feature importance analysis (Figure 5a) and the confusion matrix (Figures 6 and 7) further demonstrated how the TKEO-enhanced features enabled precise discrimination of faults, even under complex vibration patterns. This case exemplifies the framework's balance between accuracy and computational efficiency, a key requirement for real-time industrial deployment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SVC6** & **SVC4** & **RFC6** & **RFC4** \\ \hline Mean & 96.52\% & 95.10\% & 98.58\% & 97.52\% \\ SD & 0.024 & 0.045 & 0.016 & 0.026 \\ RMS & 0.965 & 0.951 & 0.985 & 0.975 \\ Kurtosis & \(-\)0.838 & 5.328 & 2.210 & 0.873 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of SVC and RFC Models with Six and Four Features. Figure 5: (**a**) Feature importance: SVC6 2400, 2 HP, 99.65% accuracy; (**b**) feature importance: SVC4 2400, 2 HP, 98,96% accuracy. Figure 6: Training confusion matrix for SVC6 2400, 2 HP, 99.65% accuracy. Figure 7: Test Confusion matrix of SVC6 2400, 2 HP, 99.65% accuracy. Based on these figures, the following can be stated regarding SVC6 (2400, 2 HP, 99.65% accuracy) and SVC4 (2400, 2 HP, 98.96% accuracy):  
* SD, RMS, and kurtosis are the most important features, with a critical role in capturing vibration signal variations and fault patterns;
* Focusing on RMS, SD, and kurtosis as primary features, with mean as a complementary feature, can further enhance model performance;
* Reducing the number of features (from 6 to 4) simplified the model but did not significantly affect its core performance, as RMS and SD remained the key contributors;
* For higher accuracy, the use of more features (Min and Max) could provide additional diagnostic insights regarding specific fault types. The confusion matrices for both the training and testing phases (Figures 6 and 7) provide a comprehensive visualization of the model's classification performance. Each row in the matrix represents the actual class, while each column represents the predicted class. The diagonal elements indicate the number of correctly classified samples, while the off-diagonal elements highlight misclassifications. For example, in Figure 6 (training phase), the diagonal value of 36 for the actual class '[EMAIL_ADDRESS]' indicates that 36 samples were correctly classified. However, the off-diagonal value of 1 in the predicted class '[EMAIL_ADDRESS]' suggests that one sample was misclassified.