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The optimized hyperparameters of the KNN algorithm_
| Hyperparameter | Range |
|---|---|
| Number of neighbors | 1 |
| Distance metric | Correlation |
| Distance weight | Inverse |
| Standardize data | true |
| Accuracy | 100 % |
Hybrid ML models_
| Applied ML model | Research description |
|---|---|
| SVM with GA | A study applied SVM combined with GA to develop optimal classifiers for distinguishing healthy and faulty bearings in ASD systems, achieving 97.5 % accuracy [21]. |
| SVM and ANN with CWT | This study explored the use of SVM and ANN alongside CWT to analyze frame vibrations during motor start-up, achieving 96.67 % accuracy with SVM and 90 % with ANN [22]. |
| PCA and SVDD | PCA and SVDD were used to predict bearing failures, achieving 93.45 % accuracy [23]. |
| GA-based SVM | A GA-based kernel discriminative feature analysis was combined with one-against-all multicategory SVMs (OAA MCSVMs) for fault diagnosis in low-speed bearings, achieving the highest reported accuracy of 98.66 % [24]. |
| FEM and WPT with SVM | A hybrid approach integrating FEM, WPT, and SVM was proposed for fault classification, achieving 81 % accuracy for inner race faults and 79 % for rolling body faults [25]. |
| FFT-based feature extraction with SVM | The frequency domain features derived from FFT were used to train an SVM model for bearing fault classification, achieving 87.35 % accuracy [26]. |
The optimized hyperparameters of the SVM algorithm_
| Hyperparameter | Value |
|---|---|
| Box constraint level | 977.88 |
| Kernel scale | 1 |
| Kernel function | Quadratic |
| Standardize data | true |
| Accuracy | 100 % |
KNN hyperparameter search range_
| Hyperparameter | Range |
|---|---|
| Number of neighbors | 1–98 |
| Distance metric | Euclidean, Cosine, Euclidean, Correlation, Chebyshev, Hamming, Minakowski, Spearman, Jaccard, City block, Mahalanobis |
| Distance weight | Equal, Inverse, Squared, Inverse |
| Standardize data | true, false |
SVM hyperparameter search range_
| Hyperparameter | Range |
|---|---|
| Box constraint level | 0.001-1000 |
| Kernel scale | 0.001-1000 |
| Kernel function | Gaussian, Linear, Quadratic, Cubic |
| Standardize data | true, false |
SVM classification_
| Model No | Kernel function | Classification success rate |
|---|---|---|
| 1 | Linear | 93.9 % |
| 2 | Polynomial (ρ = 2) | 99.5 % |
| 3 | RBF | 99 % |
The k-nearest neighbor (KNN) classification_
| Model No | Model name | Distance metric | Distance weight | Number of neighbors | Classification success rate |
|---|---|---|---|---|---|
| 1 | Cosine KNN | Cosine | Equal | 10 | 98.5 % |
| 2 | Coarse KNN | Euclidean | Equal | 100 | 74 % |
| 3 | Fine KNN | Euclidean | Equal | 1 | 97.4 % |
| 4 | Weighted KNN | Euclidean | Squared inverse | 10 | 97.4 % |
| 5 | Medium KNN | Euclidean | Equal | 1 | 98 % |
| 6 | Cubic KNN | Minkowski | Equal | 10 | 98.2 % |