Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning
Addressing the problem that it is difficult to extract the features of vibration signal and diagnose the fault of rolling bearing, we propose a novel diagnosis method combining multisynchrosqueezing S transform and faster dictionary learning (MSSST-FDL). Firstly, MSSST is adopted to transform vibrat...
Saved in:
Main Authors: | Guodong Sun, Ye Hu, Bo Wu, Hongyu Zhou |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2021-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2021/8456991 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research on Fault Diagnosis of Rolling Bearing based on Synchrosqueezing Extracting Transform
by: Qi Liu, et al.
Published: (2021-01-01) -
Fault Diagnosis of Rolling Bearings based on VMD and Symmetric Difference Energy Operator Demodulation
by: Qin Bo, et al.
Published: (2017-01-01) -
Application of KTA-KELM in Fault Diagnosis of Rolling Bearing
by: Zhuo Wang, et al.
Published: (2019-06-01) -
A New Fault Diagnosis Method for Rolling Bearings with the Basis of Swin Transformer and Generalized S Transform
by: Jin Yan, et al.
Published: (2024-12-01) -
ROLLING BEARING FAULT DIAGNOSIS BASED ON ADAPTIVE RCGMVMFE AND MANIFOLD LEARNING
by: LIU WuQiang, et al.
Published: (2022-01-01)