A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis
With the rapid development of high-speed railway, the fault diagnosis of railway vehicles has become more and more important for ensuring the operating safety. The MF is a nonlinear signal processing method which can extract the modulated faulty information via reshaping the analyzed signal. However...
Saved in:
| Main Authors: | Yan Huang, Jianhui Lin, Zechao Liu, Chenguang Huang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/2593973 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Rolling Bearing Fault Diagnosis-Optimized Scale-Space Representation for the Empirical Wavelet Transform
by: Zechao Liu, et al.
Published: (2018-01-01) -
A Fault Diagnosis Scheme for Rolling Bearing Based on Particle Swarm Optimization in Variational Mode Decomposition
by: Cancan Yi, et al.
Published: (2016-01-01) -
Bearing Fault Diagnosis in the Mixed Domain Based on Crossover-Mutation Chaotic Particle Swarm
by: Tongle Xu, et al.
Published: (2021-01-01) -
A Novel Wheelset Bearing Fault Diagnosis Method Integrated CEEMDAN, Periodic Segment Matrix, and SVD
by: Chenguang Huang, et al.
Published: (2018-01-01) -
Improved Rao-Blackwellized Particle Filter by Particle Swarm Optimization
by: Zeng-Shun Zhao, et al.
Published: (2013-01-01)