Name
Title | ||
|---|---|---|
Feature Trend Extraction and Adaptive Density Peaks Search for Intelligent Fault Diagnosis of Machines. |
Abstract | ||
|---|---|---|
Traditional machine fault diagnosis techniques are labor-intensive and hard for nonexperts to use. In this paper, a novel three-stage intelligent fault diagnosis approach is proposed for practical industrial process monitoring. A new feature processing technique is developed to enhance the identification accuracy and reduce the computation burden, which incorporates variational mode decomposition-... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TII.2018.2810226 | IEEE Transactions on Industrial Informatics |
Keywords | Field | DocType |
Clustering algorithms,Fault diagnosis,Feature extraction,Market research,Informatics,Partitioning algorithms | Data mining,Variational mode decomposition,Trend detection,Computer science,Real-time computing,Feature extraction,Bearing (mechanical),Test data,Cluster analysis,Affinity propagation clustering,Computation | Journal |
Volume | Issue | ISSN |
15 | 1 | 1551-3203 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanxue Wang | 1 | 40 | 3.61 |
| Zexian Wei | 2 | 18 | 1.04 |
| Jianwei Yang | 3 | 1 | 4.08 |