Name
Title | ||
|---|---|---|
A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning. |
Abstract | ||
|---|---|---|
•Propose a novel deep distance metric learning method for rolling bearing fault diagnosis.•Representation clustering and domain adaptation algorithms are proposed to enhance the robustness of the deep learning-based diagnosis method against environmental noises and variation of working condition.•The proposed method beats the state-of-the-art diagnosis results on a popular rolling bearing dataset.•Investigate effects of different coefficients on the diagnosis performance, and present visualizations of the learned representations. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2018.05.021 | Neurocomputing |
Keywords | Field | DocType |
Fault diagnosis,Rolling bearing,Deep learning,Deep metric learning,Environmental noise,Domain shift | Pattern recognition,Convolutional neural network,Domain adaptation,Metric (mathematics),Bearing (mechanical),Robustness (computer science),Test data,Artificial intelligence,Deep learning,Cluster analysis,Mathematics | Journal |
Volume | ISSN | Citations |
310 | 0925-2312 | 13 |
PageRank | References | Authors |
0.52 | 38 | 3 |