Name
Abstract | ||
|---|---|---|
Traditional fault diagnosis methods are easy to be affected by different working conditions. This article proposed a motor fault diagnosis method based on visual knowledge, to reduce the impact of changes in working conditions and improve the feature extraction ability. The mapping relationship between actual faults and image intuitive features by symmetrized dot pattern and scale-invariant featur... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TII.2021.3084615 | IEEE Transactions on Industrial Informatics |
Keywords | DocType | Volume |
Feature extraction,Dictionaries,Fault diagnosis,Time-frequency analysis,Visualization,Data mining,Transforms | Journal | 18 |
Issue | ISSN | Citations |
3 | 1551-3203 | 1 |
PageRank | References | Authors |
0.36 | 0 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhuo Long | 1 | 1 | 0.70 |
| Xiaofei Zhang | 2 | 2 | 1.13 |
| Min He | 3 | 1 | 1.37 |
| Shoudao Huang | 4 | 7 | 6.25 |
| Guojun Qin | 5 | 1 | 0.70 |
| Dianyi Song | 6 | 1 | 0.70 |
| Yao Tang | 7 | 1 | 0.70 |
| Gongping Wu | 8 | 32 | 10.26 |
| Weizhi Liang | 9 | 1 | 0.36 |
| Haidong Shao | 10 | 63 | 10.49 |