Name
Title | ||
|---|---|---|
Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. |
Abstract | ||
|---|---|---|
•Wavelet auto-encoder is designed with wavelet function to capture the signal characteristics.•A deep wavelet auto-encoder is constructed with multiple wavelet auto-encoders to enhance the unsupervised feature learning ability.•The proposed method effectively diagnoses the different fault types, different fault severities and different fault orientations of rolling bearing. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.knosys.2017.10.024 | Knowledge-Based Systems |
Keywords | Field | DocType |
Intelligent fault diagnosis,Rolling bearing,Deep wavelet auto-encoder,Extreme learning machine,Unsupervised feature learning | Data mining,Extreme learning machine,Computer science,Bearing (mechanical),Artificial intelligence,Deep learning,Classifier (linguistics),Wavelet,Autoencoder,Pattern recognition,Activation function,Machine learning,Feature learning | Journal |
Volume | ISSN | Citations |
140 | 0950-7051 | 11 |
PageRank | References | Authors |
0.60 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haidong Shao | 1 | 11 | 0.60 |
| Hongkai Jiang | 2 | 43 | 5.06 |
| Li Xingqiu | 3 | 21 | 2.21 |
| Wu Shuaipeng | 4 | 11 | 0.60 |