Research Article
A Damage Classification Approach for Structural Health Monitoring Using Machine Learning
Table 3
Behavior of machines with five scores per sensor (specimen 1, four sensors).
| | Machine type | UND | DMG1 | DMG2 | DMG3 | DMG4 | DMG5 | DMG6 |
| | Complex Tree | 90% | 99% | 18% | 99% | 99% | 97% | 100% |
| | Medium Tree | 90% | 99% | 18% | 99% | 99% | 97% | 100% |
| | Simple Tree | 90% | 99% | 0% | 100% | 0% | 97% | 100% |
| | Linear SVM | 97% | 100% | 100% | 99% | 99% | 99% | 100% |
| | Quadratic SVM | 97% | 100% | 100% | 99% | 99% | 99% | 100% |
| | Cubic SVM | 97% | 100% | 100% | 99% | 99% | 99% | 100% |
| | Fine Gaussian SVM | 100% | 9% | 8% | 28% | 8% | 30% | 56% |
| | Medium Gaussian SVM | 99% | 100% | 98% | 99% | 99% | 98% | 100% |
| | Coarse Gaussian SVM | 98% | 100% | 100% | 100% | 99% | 100% | 100% |
| | Fine KNN | 97% | 100% | 100% | 100% | 99% | 100% | 100% |
| | Medium KNN | 97% | 100% | 100% | 100% | 99% | 100% | 100% |
| | Coarse KNN | 93% | 100% | 100% | 99% | 97% | 100% | 100% |
| | Cosine KNN | 96% | 100% | 100% | 100% | 99% | 100% | 100% |
| | Cubic KNN | 95% | 100% | 100% | 100% | 99% | 99% | 100% |
| | Weighted KNN | 97% | 100% | 100% | 100% | 99% | 100% | 100% |
| | Boosted Trees | 90% | 100% | 0% | 100% | 0% | 100% | 100% |
| | Bagged Trees | 99% | 100% | 100% | 100% | 100% | 100% | 100% |
| | Subspace Discriminant | 98% | 100% | 100% | 100% | 99% | 100% | 100% |
| | Subspace KNN | 98% | 100% | 100% | 100% | 99% | 100% | 100% |
| | Rusboosted Trees | 90% | 100% | 0% | 0% | 0% | 0% | 0% |
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