Name
Title | ||
|---|---|---|
Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks. |
Abstract | ||
|---|---|---|
In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs is of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are limited by the complexity of mathematical modeling. Convent... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TMECH.2020.2971503 | IEEE/ASME Transactions on Mechatronics |
Keywords | DocType | Volume |
Degradation,Training,Feature extraction,Vibrations,Predictive models,Time-frequency analysis,Computational modeling | Journal | 25 |
Issue | ISSN | Citations |
3 | 1083-4435 | 2 |
PageRank | References | Authors |
0.37 | 3 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Cheng | 1 | 811 | 106.56 |
| Guijun Ma | 2 | 7 | 3.16 |
| Yong Zhang | 3 | 52 | 2.14 |
| Mingyang Sun | 4 | 14 | 8.36 |
| Fei Teng | 5 | 10 | 6.45 |
| Han Ding | 6 | 499 | 78.16 |
| Ye Yuan | 7 | 438 | 61.04 |