Name
Title | ||
|---|---|---|
A Conditional Convolutional Autoencoder Based Method for Monitoring Wind Turbine Blade Breakages |
Abstract | ||
|---|---|---|
The wind turbine blade breakage is a catastrophic failure to a wind farm. Its earlier detection is critical to prevent the unscheduled downtime and loss of whole assets. This article presents a conditional convolutional autoencoder-based monitoring method, which is of twofold, for identifying wind turbine blade breakages. First, a novel conditional convolutional autoencoder taking a multivariate s... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TII.2020.3011441 | IEEE Transactions on Industrial Informatics |
Keywords | DocType | Volume |
Blades,Monitoring,Wind farms,Condition monitoring,Data models,Principal component analysis,Wind turbines | Journal | 17 |
Issue | ISSN | Citations |
9 | 1551-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luoxiao Yang | 1 | 0 | 0.68 |
| Zijun Zhang | 2 | 13 | 3.29 |