Name
Abstract | ||
|---|---|---|
A convolutional discriminative feature learning method is presented for induction motor fault diagnosis. The approach firstly utilizes back-propagation (BP)-based neural network to learn local filters capturing discriminative information. Then, a feed-forward convolutional pooling architecture is built to extract final features through these local filters. Due to the discriminative learning of BP-... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TII.2017.2672988 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Convolution,Induction motors,Fault diagnosis,Neural networks,Feature extraction,Robustness,Support vector machines | Induction motor,Pattern recognition,Computer science,Support vector machine,Robustness (computer science),Feature extraction,Artificial intelligence,Fault Simulator,Artificial neural network,Discriminative model,Machine learning,Feature learning | Journal |
Volume | Issue | ISSN |
13 | 3 | 1551-3203 |
Citations | PageRank | References |
16 | 0.72 | 19 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenjun Sun | 1 | 16 | 0.72 |
| Rui Zhao | 2 | 145 | 9.73 |
| Ruqiang Yan | 3 | 532 | 55.59 |
| Siyu Shao | 4 | 40 | 2.51 |
| XueFeng Chen | 5 | 441 | 55.44 |