# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
## 4 Results  
The results were obtained on a Windows system using Ubuntu 20.04 LTS within the Windows Subsystem for Linux (WSL). To manage the environment, we used Anaconda 23.7.2, running Python 3.11.4 for model training. The setup included an Intel i5-13500 HX processor and an NVIDIA GeForce RTX 4060 GPU, whose powerful computational capabilities reduced the training time to under five seconds. The following results represent the highest accuracies obtained:  
* 1 HP: 745.7W (Motor power). * 2400 et 12,000: Number of samples per segment for training. * 2400 teo et 12,000 teo: Data processed with the Teager-Kaiser energy method. * SVC6 et RFC6: Results based on six features: Mean, Standard Deviation, RMS, Kurtosis, Max, Min. * SVC4 et RFC4: Results based on four features: Mean, Standard Deviation, RMS, Kurtosis. Based on the results presented in Table 1, we conducted data analysis to derive Tables 2 and 3. Table 2 and 3 lead us to the following conclusions: While the TKEO method generally showed slightly lower performance compared with traditional approaches, it demonstrated outstanding accuracy under specific conditions, such as with RFC6, 12,000 samples, and a high load (HP \(>\) 0). This highlights TKEO's potential effectiveness in scenarios involving higher mechanical loads. On the other hand, for low loads (0 HP), SVC outperformed RFC. Interestingly, with 0 HP and a smaller dataset (2400 samples), SVC achieved better accuracy than with the larger dataset (12,000 samples). Considering the number of variables, the difference between using four and six variables was generally small for the traditional methods, often within 0.6%. However, for small datasets or when using TKEO, the four-variable configuration occasionally outperformed the six-variable setup. This observation suggests that the choice of variables and dataset size has a nuanced impact on model performance, depending on the method used. As detailed in Table 4 comparing SVC and RFC models with six versus four features, the RFC models, RFC6 and RFC4, both demonstrated high average accuracy, with RFC6 achieving 98.58%. This indicates that the random forest classifier outperformed the support \begin{table}
\begin{tabular}{c c c c c c} \hline
**Load** & **Data Type** & **Support Vector Classifier** & \multicolumn{2}{c}{**Random Forest Classifier**} \\ \hline
**Load** & **Type** & **SVC6** & **SVC4** & **RFC6** & **RFC4** \\ \hline \multirow{4}{*}{0 HP} & 2400 & 99.07\% & 99.07\% & 98.25\% & 98.25\% \\  & 2400 teo & 93.93\% & 91.59\% & 97.08\% & 92.98\% \\  & 12,000 & 97.67\% & 97.67\% & 97.06\% & 100.00\% \\  & 12,000 teo & 95.35\% & 95.35\% & 94.12\% & 91.18\% \\ \hline \multirow{4}{*}{1 HP} & 2400 & 97.91\% & 97.49\% & 97.91\% & 97.91\% \\  & 2400 teo & 94.98\% & 94.14\% & 97.38\% & 95.29\% \\  & 12,000 & 95.83\% & 95.83\% & 100.00\% & 100.00\% \\  & 12,000 teo & 91.67\% & 81.25\% & 100.00\% & 94.74\% \\ \hline \multirow{4}{*}{2 HP} & 2400 & 99.65\% & 98.96\% & 99.57\% & 98.27\% \\  & 2400 teo & 97.23\% & 95.50\% & 99.13\% & 99.13\% \\  & 12,000 & 98.28\% & 98.28\% & 97.83\% & 97.83\% \\  & 12,000 teo & 94.83\% & 93.10\% & 100.00\% & 100.00\% \\ \hline \multirow{4}{*}{3 HP} & 2400 & 99.16\% & 97.91\% & 100.00\% & 99.48\% \\  & 2400 teo & 94.98\% & 93.72\% & 98.95\% & 97.91\% \\ \cline{1-1}  & 12,000 & 100.00\% & 100.00\% & 100.00\% & 97.37\% \\ \cline{1-1}  & 12,000 teo & 93.75\% & 91.67\% & 100.00\% & 100.00\% \\ \hline \end{tabular}
\end{table} 
Table 1: Results: Accuracy Percentage. \begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **SVC6** & **SVC4** & **RFC6** & **RFC4** \\ \hline
2400 & 98.95\% & 98.36\% & 98.93\% & 98.47\% \\
2400 teo & 95.28\% & 93.74\% & 98.14\% & 96.33\% \\
12,000 & 97.95\% & 97.95\% & 98.72\% & 98.80\% \\
12,000 teo & 93.90\% & 90.34\% & 98.53\% & 96.48\% \\ \hline \end{tabular}
\end{table} 
Table 2: Average Accuracy for Loads from 0 to 3 HP. vector classifier on this dataset. Additionally, the RFC models exhibited lower standard deviation; particularly, RFC6 had a standard deviation of just 0.016.