Name
Abstract | ||
|---|---|---|
Prognostics predicts the future performance progression and remaining useful life (RUL) of in-service systems based on historical and contemporary data. One of the challenges in prognostics is the development of methods that are capable of handling real-world uncertainties that typically lead to inaccurate predictions. To alleviate the impacts of uncertainties and to achieve accurate degradation t... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TIE.2021.3053882 | IEEE Transactions on Industrial Electronics |
Keywords | DocType | Volume |
Degradation,Predictive models,Prognostics and health management,Generative adversarial networks,Gallium nitride,Feature extraction,Data models | Journal | 69 |
Issue | ISSN | Citations |
1 | 0278-0046 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Huang | 1 | 0 | 0.68 |
| Yufei Tang | 2 | 203 | 22.83 |
| James VanZwieten | 3 | 0 | 0.34 |