Research Article
A Damage Classification Approach for Structural Health Monitoring Using Machine Learning
Table 2
Behavior of machines with two scores per sensor (specimen 1, four sensors).
| | Machine type | UND | DMG1 | DMG2 | DMG3 | DMG4 | DMG5 | DMG6 |
| | Complex Tree | 90% | 99% | 13% | 92% | 100% | 90% | 100% |
| | Medium Tree | 90% | 88% | 13% | 92% | 100% | 90% | 100% |
| | Simple Tree | 90% | 99% | 0% | 0% | 100% | 90% | 100% |
| | Linear SVM | 96% | 98% | 81% | 95% | 99% | 99% | 100% |
| | Quadratic SVM | 96% | 98% | 96% | 95% | 99% | 99% | 100% |
| | Cubic SVM | 96% | 99% | 98% | 95% | 99% | 99% | 100% |
| | Fine Gaussian SVM | 68% | 100% | 57% | 87% | 79% | 78% | 99% |
| | Medium Gaussian SVM | 97% | 100% | 76% | 100% | 97% | 98% | 100% |
| | Coarse Gaussian SVM | 95% | 98% | 94% | 96% | 99% | 99% | 100% |
| | Fine KNN | 97% | 100% | 96% | 98% | 99% | 100% | 100% |
| | Medium KNN | 95% | 100% | 93% | 94% | 99% | 100% | 100% |
| | Coarse KNN | 91% | 100% | 85% | 80% | 99% | 100% | 94% |
| | Cosine KNN | 95% | 100% | 74% | 89% | 99% | 100% | 100% |
| | Cubic KNN | 95% | 99% | 89% | 93% | 99% | 99% | 100% |
| | Weighted KNN | 95% | 100% | 95% | 97% | 99% | 100% | 100% |
| | Boosted Trees | 90% | 100% | 20% | 1% | 100% | 98% | 100% |
| | Bagged Trees | 99% | 100% | 71% | 95% | 100% | 100% | 100% |
| | Subspace Discriminant | 97% | 100% | 64% | 97% | 100% | 100% | 100% |
| | Subspace KNN | 97% | 100% | 82% | 98% | 100% | 100% | 100% |
| | Rusboosted Trees | 90% | 100% | 0% | 0% | 0% | 0% | 0% |
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