Research Article
Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning
Table 2
Description of 12 kHz drive-end bearing datasets.
| Fault location | None | RF | IF | OF |
| Category labels | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Fault diameter (inch) | 0 | 0.007 | 0.014 | 0.021 | 0.007 | 0.014 | 0.021 | 0.007 | 0.014 | 0.021 | Dataset A (1 HP) | Train | 660 | 660 | 660 | 660 | 660 | 660 | 660 | 660 | 660 | 660 | Test | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | Dataset B (2 HP) | Train | 660 | 660 | 660 | 660 | 660 | 660 | 660 | 660 | 660 | 660 | Test | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | Dataset C (3 HP) | Train | 660 | 660 | 660 | 660 | 660 | 660 | 660 | 660 | 660 | 660 | Test | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 | 25 |
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