Name
Abstract | ||
|---|---|---|
Remaining useful life (RUL) prediction is extremely significant to ensure the safe and reliable operation for bearing suffering from the deterioration. The main focus of the RUL prediction is to accurately predict the future failure event, and thus, how to quantify the prediction uncertainty will be a major concern. However, current deep learning based RUL prediction methods are difficult to refle... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TIE.2019.2947839 | IEEE Transactions on Industrial Electronics |
Keywords | DocType | Volume |
Feature extraction,Indexes,Uncertainty,Degradation,Machine learning,Artificial neural networks,Reliability | Journal | 67 |
Issue | ISSN | Citations |
10 | 0278-0046 | 6 |
PageRank | References | Authors |
0.43 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| C. H. Chang | 1 | 428 | 36.69 |
| Hong Pei | 2 | 6 | 0.43 |
| Xiao-Sheng Si | 3 | 623 | 46.17 |
| Dang-Bo Du | 4 | 11 | 3.91 |
| Zhenan Pang | 5 | 7 | 3.50 |
| Xi Wang | 6 | 6 | 0.43 |