Name
Abstract | ||
|---|---|---|
Effective fault diagnosis of rotating machinery has multifarious benefits, such as improved safety, enhanced reliability, and reduced maintenance cost, for complex engineered systems. With many kinds of installed sensors for conducting fault diagnosis, one of the key tasks is to develop data fusion strategies that can effectively handle multimodal sensory signals. Most traditional methods use hand... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TII.2018.2793246 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Fault diagnosis,Couplings,Feature extraction,Data integration,Sensors,Vibrations,Data models | Data integration,Data modeling,Coupling,Autoencoder,Pattern recognition,Computer science,Real-time computing,Feature extraction,Sensor fusion,Artificial intelligence,Concatenation,Sensory system | Journal |
Volume | Issue | ISSN |
14 | 3 | 1551-3203 |
Citations | PageRank | References |
13 | 0.59 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Ma | 1 | 82 | 12.29 |
| Chuang Sun | 2 | 70 | 8.35 |
| XueFeng Chen | 3 | 441 | 55.44 |