Name
Title | ||
|---|---|---|
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox. |
Abstract | ||
|---|---|---|
This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches, where feature extraction and classification are separately designed and performed, this paper aims to automatically learn effective fault features directly from raw vibration signals while classify the type of faults in a ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIE.2018.2844805 | IEEE Transactions on Industrial Electronics |
Keywords | Field | DocType |
Feature extraction,Fault diagnosis,Vibrations,Convolutional neural networks,Wind turbines,Machine learning,Signal processing | Signal processing,Pattern recognition,Convolutional neural network,Pooling,Control engineering,Feature extraction,Artificial intelligence,Turbine,Engineering,Vibration,Wind power,Feature learning | Journal |
Volume | Issue | ISSN |
66 | 4 | 0278-0046 |
Citations | PageRank | References |
14 | 0.67 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoqian Jiang | 1 | 210 | 50.15 |
| Haibo He | 2 | 3653 | 213.96 |
| Jun Yan | 3 | 179 | 13.72 |
| Ping Xie | 4 | 40 | 17.27 |