Name
Title | ||
|---|---|---|
Intelligent Fault Diagnosis Method Based on Full 1-D Convolutional Generative Adversarial Network. |
Abstract | ||
|---|---|---|
Data-driven fault diagnosis is essential for the reliability and safety of industry equipment. However, the lack of real labeled fault data make the machine learning-based diagnosis methods difficult to carry out. To solve this problem, this article proposes a new fault diagnosis framework called multilabel one-dimensional (1-D) generation adversarial network (ML1-D-GAN). In our method, Auxiliary ... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TII.2019.2934901 | IEEE Transactions on Industrial Informatics |
Keywords | DocType | Volume |
Fault diagnosis,Feature extraction,Convolution,Training,Data models,Gallium nitride,Generators | Journal | 16 |
Issue | ISSN | Citations |
3 | 1551-3203 | 11 |
PageRank | References | Authors |
0.53 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingwen Guo | 1 | 11 | 0.53 |
| Yibin Li | 2 | 75 | 3.55 |
| Yan Song | 3 | 284 | 53.62 |
| Daichao Wang | 4 | 11 | 0.53 |
| Wu Chen | 5 | 11 | 0.53 |