Name
Abstract | ||
|---|---|---|
This article proposes a fault diagnosis (FD) method that is based on bispectrum analysis and a convolutional neural network (CNN) to identify bearing faults under inconsistent working conditions, such as high shaft speed variations with cracks of multiple scales and compound faults. First, the bispectra of the vibration signals are extracted, which exhibit consistent patterns under inconsistent wo... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TIM.2019.2933342 | IEEE Transactions on Instrumentation and Measurement |
Keywords | DocType | Volume |
Fault diagnosis,Vibrations,Employee welfare,Shafts,Feature extraction,Support vector machines,Artificial neural networks | Journal | 69 |
Issue | ISSN | Citations |
6 | 0018-9456 | 2 |
PageRank | References | Authors |
0.37 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Sohaib | 1 | 2 | 0.37 |
| Jong Myon Kim | 2 | 144 | 32.36 |