Name
Title | ||
|---|---|---|
Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks. |
Abstract | ||
|---|---|---|
•CatGAN and AAE are introduced in unsupervised fault diagnosis of rolling bearings for their great ability of unsupervised clustering and mapping respectively.•By adding a classifier on the latent layer of AAE, we propose a new model named CatAAE for unsupervised clustering and exhibit the better performance compared with other methods.•Mixed time-frequency features are employed in the method to get a better robustness under different environments.•Considering about the expenses of labeling data, the proposed unsupervised method is more practical for application. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2018.07.034 | Neurocomputing |
Keywords | Field | DocType |
Unsupervised fault diagnosis,Generative adversarial networks,Adversarial autoencoders,Categorical generative adversarial networks,Unsupervised clustering | Autoencoder,Categorical variable,Robustness (computer science),Mutual information,Artificial intelligence,Prior probability,Cluster analysis,Classifier (linguistics),Artificial neural network,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
315 | 0925-2312 | 3 |
PageRank | References | Authors |
0.41 | 16 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han Liu | 1 | 3 | 0.74 |
| Jianzhong Zhou | 2 | 511 | 55.54 |
| Yanhe Xu | 3 | 18 | 5.39 |
| Yang Zheng | 4 | 7 | 2.56 |
| Xuanlin Peng | 5 | 3 | 0.41 |
| Wei Jiang | 6 | 5 | 2.81 |