A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine
A rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained. Next, time-domain index, energy, and...
Saved in:
Main Authors: | Mingliang Liang, Dongmin Su, Daidi Hu, Mingtao Ge |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2018-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2018/1891453 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM
by: Jiang Xingmeng, et al.
Published: (2016-01-01) -
Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features
by: Weigang Wen, et al.
Published: (2015-01-01) -
A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing
by: Hu Aijun, et al.
Published: (2017-01-01) -
The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest
by: Xiwen Qin, et al.
Published: (2021-01-01) -
Rolling Bearing Fault Diagnosis Method Based on MCMF and SAIMFE
by: Dejun Meng, et al.
Published: (2022-01-01)