# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
## 5 Discussion
### Summary and Recommendations  
To contextualize our contributions, Table 5 synthesizes critical methodological choices and performance outcomes from recent studies on bearing fault diagnosis. The comparison focuses on four dimensions: (1) feature extraction strategies, (2) classifier architectures, (3) dataset utilization, and (4) accuracy-class complexity tradeoffs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Datasets** & **Feature Extraction** & **Features** & **Classifier** & **Accuracy** & **Classes of Problem** \\ \hline \multirow{3}{*}{[PERSON] et al. (2022) [6]} & \multirow{3}{*}{CWRU} & Downsample signals and generate spectrogram using STFT & \multirow{3}{*}{Spectrogram} & \multirow{3}{*}{Line CNN} & \multirow{3}{*}{99.92\%} & \multirow{3}{*}{10 classes} \\ \cline{1-1} \cline{5-6} \cline{8-8} \cline{7}  & & & & & \\ \hline \multirow{3}{*}{Souisdia and Thelaidjia (2020) [7]} & \multirow{3}{*}{CWRU} & Statistical features & \multirow{3}{*}{9 statistical features} & \multirow{3}{*}{LR, RF, SVM} & \multirow{3}{*}{LR-99.02\%} & \multirow{3}{*}{10 classes} \\  & Thelaidjia (2020) [7] & & & & & \\ \cline{5-6} \cline{8-8} \cline{7}  & & Extract amplitude & & & & \\ \cline{5-6} \cline{8-8} \cline{7}  & & information & & & & \\ \cline{5-6} \cline{8-8} \cline{7}  & & envelope and frequency- & & & & \\ \cline{5-6} \cline{8-8} \cline{7}  & & domain analysis & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. (2023) [28]} & \multirow{3}{*}{CWRU} & Normalization, data balancing, and categorical encoding & 9 time-domain/frequency-domain features & RF (PyCaret) and & Training 99.70\% & \multirow{3}{*}{10 classes} \\  & & & & & & \\ \cline{5-6} \cline{8-8} \cline{7}  & & Improved TKCD method; & & & & \\ \cline{5-6} \cline{8-8} \cline{7}  & & CWRU, combined bandpass & Components & \multirow{3}{*}{Not specified (focus & \multirow{3}{*}{2 classes (early fault/normal)} \\ \cline{5-6} \cline{8-8} \cline{7} \cline{8-8} \cl  
In summary, under the experimental conditions of the current study, the random forest classifier provided a good balance between ease of implementation, robustness, and computational complexity; particularly, it demonstrated stability in the presence of noise. However, the computational complexity of the RFC increased linearly with the size of the dataset, suggesting that more computational resources may be needed for large-scale datasets.  
On the other hand, support vector classifiers offer better classification performance on smaller datasets, especially when the data distribution is more complex. An SVC is capable of finding the optimal hyperplane in high-dimensional feature space. However, SVMs require more stringent data preprocessing and hyperparameter tuning, and their computational complexity significantly increases with the growth of the dataset.  
Ultimately, the choice of algorithm should be based on the specific application scenario and experimental requirements. If the focus is on tasks involving noisy environments with lower computational resource demands, RFC is a better choice. If the dataset is smaller and higher classification accuracy is required, an SVC may perform better.  
While the TKEO method may show slightly lower performance compared with traditional approaches in general, its computational efficiency makes it particularly advantageous when working with large datasets. This is because TKEO requires fewer calculations, making it more suitable for scenarios where processing time and computational resources are critical. Moreover, under specific conditions, such as high mechanical load (HP > 0) or larger datasets (e.g., 12,000 samples), TKEO demonstrated strong accuracy, showing its potential to be highly effective in those settings.