Name
Title | ||
|---|---|---|
KDnet-RUL: A Knowledge Distillation Framework to Compress Deep Neural Networks for Machine Remaining Useful Life Prediction |
Abstract | ||
|---|---|---|
Machine remaining useful life (RUL) prediction is vital in improving the reliability of industrial systems and reducing maintenance cost. Recently, long short-term memory (LSTM) based algorithms have achieved state-of-the-art performance for RUL prediction due to their strong capability of modeling sequential sensory data. In many cases, the RUL prediction algorithms are required to be deployed on... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TIE.2021.3057030 | IEEE Transactions on Industrial Electronics |
Keywords | DocType | Volume |
Predictive models,Data models,Knowledge engineering,Feature extraction,Prediction algorithms,Deep learning,Neural networks | Journal | 69 |
Issue | ISSN | Citations |
2 | 0278-0046 | 3 |
PageRank | References | Authors |
0.38 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qing Xu | 1 | 3 | 0.38 |
| Chen Zhenghua | 2 | 141 | 10.59 |
| Keyu Wu | 3 | 3 | 0.72 |
| Chao Wang | 4 | 3 | 0.38 |
| Min Wu | 5 | 3 | 0.38 |
| Xiao-Li Li | 6 | 74 | 9.21 |