Research Article
Fault Diagnosis of Rotating Machinery Based on Multisensor Information Fusion Using SVM and Time-Domain Features
Table 2
Diagnostic results of gear by using different features for fusion.
| Feature |
The best parameter | Diagnostic accuracy (%) | | | Normal | Chipped tooth | Missing tooth | All testing samples |
| Mean | 26.5 | 215 | 98 | 82 | 84 | 88.00 | Peak | 21 | 28 | 98 | 86 | 66 | 83.33 | Amplitude square | 212 | 20 | 98 | 82 | 88 | 89.33 | Root mean square | 214 | 23 | 98 | 78 | 86 | 87.33 | Root amplitude | 211.5 | 211.5 | 100 | 88 | 76 | 88.00 | Standard deviation | 27.5 | 29.5 | 98 | 78 | 86 | 87.33 | Skewness | 28 | 2−1 | 98 | 30 | 58 | 62.00 | Kurtosis | 22.5 | 2−3 | 94 | 68 | 40 | 67.33 | Waveform factor | 2−1.5 | 29 | 92 | 42 | 30 | 55.33 | Peak factor | 20.5 | 2−1 | 94 | 92 | 94 | 93.33 | Pulse factor | 2−0.5 | 2−2 | 96 | 64 | 60 | 73.33 | Margin factor | 20 | 2−3 | 96 | 57 | 55 | 69.33 |
|
|