Name
Title | ||
|---|---|---|
LiftingNet: A Novel Deep Learning Network With Layerwise Feature Learning From Noisy Mechanical Data for Fault Classification. |
Abstract | ||
|---|---|---|
The key challenge of intelligent fault diagnosis is to develop features that can distinguish different categories. Because of the unique properties of mechanical data, predetermined features based on prior knowledge are usually used as inputs for fault classification. However, proper selection of features often requires expertise knowledge and becomes more difficult and time consuming when volume ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIE.2017.2767540 | IEEE Transactions on Industrial Electronics |
Keywords | Field | DocType |
Machine learning,Fault diagnosis,Feature extraction,Neural networks,Convolution,Transforms,Support vector machines | Data mining,Pattern recognition,Computer science,Convolutional neural network,Deep belief network,Support vector machine,Second-generation wavelet transform,Feature extraction,Artificial intelligence,Deep learning,Artificial neural network,Feature learning | Journal |
Volume | Issue | ISSN |
65 | 6 | 0278-0046 |
Citations | PageRank | References |
15 | 0.62 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Pan | 1 | 16 | 0.98 |
| Yanyang Zi | 2 | 268 | 25.13 |
| Jinglong Chen | 3 | 28 | 8.24 |
| Zitong Zhou | 4 | 18 | 5.08 |
| Biao Wang | 5 | 82 | 17.14 |