Research Article
An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis
Table 5
Accuracies of SVM classifier under various kernels of KPCA and mother wavelets.
| Mother wavelet |
Level
| KPCA with linear kernel | KPCA with RBF kernel and | KPCA with polynomial kernel and | Accuracy | Feature number | Accuracy | Feature number | Accuracy | Feature number |
| Db3 | 3 | 95.48% | 34 | 91.12% | 86 | 67.22% | 22 | 4 | 94.20% | 62 | 90.08% | 110 | 64.20% | 28 | 5 | 95.13% | 110 | 81.24% | 132 | 87.36% | 45 |
| Db4 | 3 | 95.12% | 25 | 88.24% | 87 | 70.00% | 18 | 4 | 96.26% | 54 | 87.34% | 106 | 81.00 % | 27 | 5 | 95.55% | 105 | 83.42% | 132 | 74.26% | 39 |
| Db5 | 3 | 84.40% | 26 | 82.53% | 89 | 66.36% | 20 | 4 | 93.23% | 54 | 85.32% | 110 | 64.06% | 27 | 5 | 93.49% | 105 | 80.00% | 132 | 81.04% | 38 |
| Db6 | 3 | 94.20% | 26 | 84.05% | 98 | 68.34% | 19 | 4 | 95.32% | 53 | 85.64% | 104 | 66.78% | 24 | 5 | 93.78% | 102 | 88.04% | 130 | 85.30% | 38 |
| Average | | 93.85% | — | 85.59% | — | 73% | — |
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