Research Article
A New Framework Based on Supervised Joint Distribution Adaptation for Bearing Fault Diagnosis across Diverse Working Conditions
Table 7
Comparison of experimental results between DFCD-IDAMN and relevant methods from other literatures.
| Abbreviation of methods | DA method | Literature | Experimental data | Cross-domain tasks | Maximum mean accuracy (%) |
| DFCD-IDAMN | IDAMN | This article | Bearing data from CWRU and our own test-bed | 12 tasks under 4 working conditions | 99.57 |
| MTSDE [56] | MMD | A novel cross-domain intelligent fault diagnosis method based on entropy features and transfer learning | Bearing from PHM2009, CWRU, and MFPT | 6 diagnosis tasks under 2 working conditions | 97.10 |
| BARTL [3] | BDA | Balanced adaptation regularization based transfer learning for unsupervised cross-domain fault diagnosis | Bearing data from Jiangnan university and Politecnico di Torino | 6 diagnosis tasks under 2 working speeds | 98.73 |
| FT-IDJ [57] | JDA | An intelligent fault diagnosis method for rolling bearings based on feature transfer with improved DenseNet and joint distribution adaptation | Bearing data from CWRU | 12 diagnosis tasks under 4 working speeds | 98.50 |
| TCA-based [58] | TCA | Transfer learning based data feature transfer for fault diagnosis | Bearing data from CWRU | 6 diagnosis tasks under 2 working speeds | 91.40 |
| AMPD [26] | GFK | A new transferable bearing fault diagnosis method with adaptive manifold probability distribution under different working conditions | Bearing data from own test rig | 12 diagnosis tasks under 4 working speeds | 98.85 |
| JGSA-FTFE [59] | JGSA | Time frequency feature analysis of rolling bearing fault based on deep transfer learning | Bearing data from CWRU and own test-bed | 2 diagnosis tasks under 2 working conditions | 95.55 |
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The bold values highlight that the experimental results are desirable.
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