Name
Title | ||
|---|---|---|
A novel sub-label learning mechanism for enhanced cross-domain fault diagnosis of rotating machinery |
Abstract | ||
|---|---|---|
•A sub-label learning mechanism is proposed to enhance domain adaptability.•Traditional domain adaptation ignores the correlation between samples.•Exploring structural connectivity can effectively reduce mismatches.•The proposed method is insensitive to trade-off parameters. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.ress.2022.108589 | Reliability Engineering & System Safety |
Keywords | DocType | Volume |
Domain adaptation,Transfer learning,Statistical moment matching,Adversarial training,Fault diagnosis,Rotating machinery | Journal | 225 |
ISSN | Citations | PageRank |
0951-8320 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minqiang Deng | 1 | 0 | 0.68 |
| Aidong Deng | 2 | 1 | 2.04 |
| Yaowei Shi | 3 | 0 | 0.68 |
| Yang Liu | 4 | 0 | 0.68 |
| Meng Xu | 5 | 0 | 0.68 |