Research Article
A Deep Domain-Adversarial Transfer Fault Diagnosis Method for Rolling Bearing Based on Ensemble Empirical Mode Decomposition
Table 4
Test results of different IMFs input signals.
| Working condition | IMF1-IMF3 | IMF1-IMF5 | IMF1-IMF6 | IMF1-IMF4 |
| A->B | 94.17% | 96.67% | 97.83% | 97.83% | A->C | 93.50% | 95.33% | 96.50% | 97.33% | A->D | 94.00% | 96.83% | 97.33% | 96.50% | B->A | 92.83% | 94.83% | 95.83% | 96.67% | B->C | 95.83% | 98.33% | 99.33% | 100% | B->D | 94.67% | 97.83% | 97.50% | 97.17% | C->A | 92.33% | 94.33% | 94.83% | 96.50% | C->B | 92.83% | 98.83% | 97.33% | 97.33% | C->D | 94.83% | 97.17% | 98.67% | 99.50% | D->A | 93.17% | 95.83% | 96.33% | 97.17% | D->B | 94.50% | 96.17% | 98.00% | 97.33% | D->C | 95.17% | 96.67% | 98.83% | 99.83% | Average | 93.99% | 96.18% | 97.36% | 98.17% |
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