Research Article
Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition
Table 3
The detailed information of the second group dataset.
| Bearing condition | Defect diameter (mm) | Number of training samples | Number of testing samples | Class label | 3 hp | 3 hp (case 3) | 2 hp (case 4) |
| Normal | 0 | 20 | 40 | 40 | 1 |
| Ball fault | 0.007 | 20 | 40 | 40 | 2 | 0.014 | 20 | 40 | 40 | 3 | 0.021 | 20 | 40 | 40 | 4 | 0.028 | 20 | 40 | 40 | 5 |
| Inner race fault | 0.007 | 20 | 40 | 40 | 6 | 0.014 | 20 | 40 | 40 | 7 | 0.021 | 20 | 40 | 40 | 8 | 0.028 | 20 | 40 | 40 | 9 |
| Outer race fault | 0.007 | 20 | 40 | 40 | 10 | 0.014 | 20 | 40 | 40 | 11 | 0.021 | 20 | 40 | 40 | 12 |
| Number of samples | | 240 | 480 | 480 | |
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