Name
Title | ||
|---|---|---|
Stacked Multilevel-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis. |
Abstract | ||
|---|---|---|
Currently, vibration analysis has been widely considered as an effective way to fulfill the fault diagnosis task of gearboxes in wind turbines (WTs). However, vibration signals are usually with abundant noise and characterized as nonlinearity and nonstationarity. Therefore, it is quite challenging to extract robust and useful fault features from complex vibration signals to achieve an accurate and... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TIM.2017.2698738 | IEEE Transactions on Instrumentation and Measurement |
Keywords | Field | DocType |
Feature extraction,Fault diagnosis,Vibrations,Training,Robustness,Noise reduction,Wind turbines | Noise reduction,Network architecture,Electronic engineering,Robustness (computer science),Feature extraction,Artificial intelligence,Vibration,Discriminative model,Mathematics,Machine learning,Wind power,Feature learning | Journal |
Volume | Issue | ISSN |
66 | 9 | 0018-9456 |
Citations | PageRank | References |
5 | 0.44 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoqian Jiang | 1 | 210 | 50.15 |
| Haibo He | 2 | 3653 | 213.96 |
| Ping Xie | 3 | 40 | 17.27 |
| Yufei Tang | 4 | 203 | 22.83 |