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SVM hyper parameters for BPF - single node testing_
| Optimization method | Fault detection accuracy | Hyper parameters | |
|---|---|---|---|
| Kernel | Accuracy [%] | ||
| Random search | linear | 65 | C = 100 |
| rbf | 80 | C = 100, γ = 0.012 | |
| poly | 55 | C = 100, d = 2 | |
| sigmoid | 80 | C = 100, γ = 0.012 | |
| Bayesian search (number of iterations = 100) | linear | 70 | C = 223.64 |
| rbf | 80 | C = 63.65, γ = 1.82 | |
| poly | 75 | C = 6.01, d = 3 | |
| sigmoid | 75 | C = 338.98, γ = 0.575 | |
| PSO (number of iterations = 100, particle size = 10) | linear | 65 | C = 412 |
| rbf | 80 | C = 921.4, γ = 6.9 | |
| poly | 55 | C = 844.5, d = 2 | |
| sigmoid | 70 | C = 307.3, γ = 0.12 | |
| Exhaustive search | linear | 65 | C = 100 |
| rbf | 90 | C = 100, γ = 0.0127 | |
| poly | 70 | C = 1000, d = 2 | |
| sigmoid | 55 | C = 1000, γ = 0.1 | |
SVF SVM hyper parameters - single node testing
| Optimization method | Fault detection accuracy | Hyper parameters | |
|---|---|---|---|
| Kernel | Accuracy [%] | ||
| Random search | linear | 22.22 | C = 4.4 |
| rbf | 11.11 | C = 100, γ = 0.01 | |
| poly | 11.11 | C = 35.93, d = 2 | |
| sigmoid | 22.22 | C = 100, γ = 0.01 | |
| Bayesian search (number of iterations = 100) | linear | 33.33 | C = 1.179 |
| rbf | 16.67 | C = 3.08, γ = 0.243 | |
| poly | 5.56 | C = 74.63, d = 2 | |
| sigmoid | 16.67 | C = 1000, γ = 0.0013 | |
| PSO (number of iterations = 100, particle size = 10) | linear | 22.22 | C = 887.42 |
| rbf | 22.22 | C = 267, γ = 0.101 | |
| poly | 16.67 | C = 256, d = 2 | |
| sigmoid | 16.67 | C = 154.4, γ = 0.022 | |
| Exhaustive search | linear | 22.22 | C = 100 |
| rbf | 50 | C = 100, γ = 0.092 | |
| poly | 50 | C = 1000, d = 2 | |
| sigmoid | 22.22 | C = 1000, γ = 0.01 | |
SVM hyper parameters for BPF_
| Optimization method | Fault detection accuracy | Hyper parameters | |
|---|---|---|---|
| Kernel | Accuracy [%] | ||
| Random search | linear | 95 | C = 100 |
| rbf | 95 | C = 100, γ = 0.0085 | |
| poly | 65 | C = 100, d = 3 | |
| sigmoid | 80 | C = 100, γ = 0.0085 | |
| Bayesian search (number of iterations = 100) | linear | 92 | C = 1000 |
| rbf | 95 | C = 1.913, γ = 0.9 | |
| poly | 90 | C = 86.32, d = 3 | |
| sigmoid | 90 | C = 1000, γ = 0.0417 | |
| PSO (number of iterations = 100, particle size = 10) | linear | 96 | C = 831 |
| rbf | 95 | C = 200.42, γ = 6.2 | |
| poly | 95 | C = 256, d = 3 | |
| sigmoid | 85 | C = 218.57, γ = 0.111 | |
| Exhaustive search | linear | 100 | C = 1000 |
| rbf | 100 | C = 1000, γ = 0.00857 | |
| poly | 95 | C = 1000, d = 2 | |
| sigmoid | 45 | C = 1000, γ = 0.1 | |
SVF SVM hyper parameters_
| Optimization method | Fault detection accuracy | Hyper parameters | |
|---|---|---|---|
| Kernel | Accuracy [%] | ||
| Random search | linear | 22.22 | C = 12.9 |
| rbf | 50 | C = 100, γ = 0.034 | |
| poly | 27.78 | C = 100, d = 2 | |
| sigmoid | 27.78 | C = 100, γ = 0.01 | |
| Bayesian search (number of iterations = 100) | linear | 55.6 | C = 157.8 |
| rbf | 66.67 | C = 1000, γ = 0.207 | |
| poly | 66.67 | C = 1000, d = 3 | |
| sigmoid | 44.44 | C = 769.83, γ = 0.0086 | |
| PSO (number of iterations = 100, particle size = 10) | linear | 50 | C = 191.37 |
| rbf | 50 | C = 267.13, γ = 0.172 | |
| poly | 37 | C = 152, d = 3 | |
| sigmoid | 27.78 | C = 261.3, γ = 0.0158 | |
| Exhaustive search | linear | 88 | C = 100 |
| rbf | 90 | C = 1000, γ = 0.046 | |
| poly | 89 | C = 1000, d = 3 | |
| sigmoid | 28 | C = 1000, γ = 0.01 | |