Name
Abstract | ||
|---|---|---|
•A novel domain adaptation method is proposed to solve domain shift problem.•Multi-Layer and multi-kernel MMD between source and target domains is minimized.•Cross-domain fault diagnosis performance on rolling bearings is significantly improved.•The proposed method is promising for applications in different industrial scenarios. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.sigpro.2018.12.005 | Signal Processing |
Keywords | Field | DocType |
Fault diagnosis,Domain adaptation,Deep learning,Maximum mean discrepancy,Rolling bearing | Signal processing,Mathematical optimization,Multi layer,Convolutional neural network,Domain adaptation,Bearing (mechanical),Supervised learning,Artificial intelligence,Test data,Deep learning,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
157 | 0165-1684 | 10 |
PageRank | References | Authors |
0.63 | 32 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiang Li | 1 | 56 | 6.55 |
| Wei Zhang | 2 | 41 | 2.58 |
| Qian Ding | 3 | 46 | 2.65 |
| Jian-Qiao Sun | 4 | 10 | 0.63 |