Name
Title | ||
|---|---|---|
Data augmentation for fault prediction of aircraft engine with generative adversarial networks |
Abstract | ||
|---|---|---|
Fault prediction is to predict the remaining useful life(RUL) of the equipment by constructing models using historical data. However, the run-to-failure data is difficult to obtain, and it is impossible to build an accurate prediction model. To address this problem, an innovative forecasting method based on data augmentation technology is proposed in this paper. First, a generative adversarial network (GAN) is used to study the distribution of the original dataset, and generate a new train set. Then, the original dataset is fused with the new one to train a convolutional neural network and long short-term memory network (CNN-LSTM) prediction model. Finally, the experiment is conducted on the original C-MAPSS and fusion dataset, which results show that the proposed feature extraction method can effectively predict the RUL compared with the existed methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/SAFEPROCESS52771.2021.9693711 | 2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS) |
Keywords | DocType | ISBN |
fault predict,generative adversarial networks,data augmentation,convolutional neural networks,long and short-term memory | Conference | 978-1-6654-0116-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengxue Lang | 1 | 0 | 0.34 |
| Kaixiang Peng | 2 | 53 | 12.22 |
| Jiapeng Cui | 3 | 0 | 0.34 |
| Jie Yang | 4 | 0 | 0.34 |
| Yingxin Guo | 5 | 0 | 0.34 |