Name
Title | ||
|---|---|---|
Rolling bearing fault detection using continuous deep belief network with locally linear embedding. |
Abstract | ||
|---|---|---|
•A new comprehensive feature index is defined to quantify bearing performance degradation.•A continuous deep belief network is constructed to model vibration signals.•The key parameters of the continuous deep belief network are optimized with genetic algorithm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.compind.2018.01.005 | Computers in Industry |
Keywords | Field | DocType |
Continuous deep belief network,Rolling bearing,Fault detection,Comprehensive feature index,Genetic algorithm optimization | Boltzmann machine,Embedding,Deep belief network,Algorithm,Control engineering,Bearing (mechanical),Bearing fault detection,Vibration,Engineering,Genetic algorithm | Journal |
Volume | ISSN | Citations |
96 | 0166-3615 | 5 |
PageRank | References | Authors |
0.43 | 29 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haidong Shao | 1 | 63 | 10.49 |
| Hongkai Jiang | 2 | 43 | 5.06 |
| Li Xingqiu | 3 | 21 | 2.21 |
| Tianchen Liang | 4 | 18 | 1.08 |