Research Article
Fault Diagnosis of Rotating Machinery Based on Multisensor Information Fusion Using SVM and Time-Domain Features
Table 3
Diagnostic results of gear by using different single sensors.
| Sensor |
The best parameter | Diagnostic accuracy (%) | | | Normal | Chipped tooth | Missing tooth | All testing samples |
| s
1 | 25.5 | 28 | 40 | 28 | 86 | 51.33 | s
2 | 23.5 | 213.5 | 22 | 94 | 100 | 72.00 | s
3 | 21 | 24 | 84 | 82 | 76 | 80.67 | s
4 | 23 | 28.5 | 54 | 80 | 94 | 76.00 | s
5 | 215 | 20.5 | 64 | 90 | 100 | 84.67 | s
6 | 213.5 | 26.5 | 92 | 96 | 56 | 81.33 | s
7 | 211.5 | 27.5 | 90 | 96 | 64 | 83.33 | s
8 | 215 | 24 | 78 | 86 | 58 | 74.00 |
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