Name
Title | ||
|---|---|---|
Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation. |
Abstract | ||
|---|---|---|
•The challenging cross-machine transfer learning problem in fault diagnosis is investigated.•The machine-invariant features are extracted using deep auto-encoder, and domain adaptation is used for feature alignment.•The practical scenarios in fault diagnosis are considered where only the target-machine data in healthy state are available .•Different fault locations and severities are both considered in the cross-machine fault diagnosis.•Experiments on three rotating machinery datasets validate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2019.12.033 | Neurocomputing |
Keywords | Field | DocType |
Deep learning,Fault diagnosis,Model generalization,Auto-encoder,Rolling bearing | Adaptability,Autoencoder,Subspace topology,Domain adaptation,Transfer of learning,Bearing (mechanical),Artificial intelligence,Test data,Deep learning,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
383 | 0925-2312 | 1 |
PageRank | References | Authors |
0.37 | 0 | 6 |