Name
Title | ||
|---|---|---|
A reinforcement ensemble deep transfer learning network for rolling bearing fault diagnosis with Multi-source domains |
Abstract | ||
|---|---|---|
Fault diagnosis with transfer learning has achieved great attention. However, existing methods mostly focused on single-source-single-target sceneries. In some cases, there may exist multiple source domains. Therefore, a reinforcement ensemble deep transfer learning network (REDTLN) is proposed for fault diagnosis with multi-source domains. Firstly, various new kernel maximum mean discrepancies (kMMDs) are used to construct multiple deep transfer learning networks (DTLNs) for single-source-single-target domain adaptation. The differences of kernel functions and source domains can help the DTLNs learn diverse transferable features. Secondly, a new unified metric is designed based on kMMD and diversity measures for unsupervised ensemble learning. Finally, using the unified metric as the reward, a reinforcement learning method is firstly explored to generate an effective combination rule for multi-domain-multi-model reinforcement ensemble. The proposed method is verified with experiment datasets, and the results empirically show its effectiveness and superiority compared with other methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.aei.2021.101480 | Advanced Engineering Informatics |
Keywords | DocType | Volume |
Rolling bearing,Fault diagnosis,Multi-source domains,Reinforcement ensemble deep transfer network,Unified metric | Journal | 51 |
ISSN | Citations | PageRank |
1474-0346 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xingqiu Li | 1 | 0 | 0.34 |
| Hongkai Jiang | 2 | 43 | 5.06 |
| Min Xie | 3 | 1263 | 96.98 |
| Tongqing Wang | 4 | 0 | 0.34 |
| Ruixin Wang | 5 | 5 | 1.52 |
| Zhenghong Wu | 6 | 2 | 1.39 |