A Novel Fault Diagnosis Method for Motor Bearing Based on DTCWT and AFSO-KELM
Aiming at the defects of wavelet transform-based feature extraction and extreme learning machine-based classification, a novel fault diagnosis method for motor bearing, based on dual tree complex wavelet transform and artificial fish swarm optimization-kernel extreme learning machine (DTCWT-AFSO-KEL...
Saved in:
Main Authors: | Yan Lu, Peijiang Li |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2021-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2021/2108457 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Application of KTA-KELM in Fault Diagnosis of Rolling Bearing
by: Zhuo Wang, et al.
Published: (2019-06-01) -
Application of EMPE and KP-KELM in Fault Diagnosis of Planetary Gearbox
by: Bo Qin, et al.
Published: (2019-05-01) -
Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM
by: Zhao Xiaohui, et al.
Published: (2023-02-01) -
Fault Diagnosis of Wind Turbine Gearbox based on LSGAN and VMD-MPE-KELM
by: Cihai Qin, et al.
Published: (2021-11-01) -
A CBA-KELM-Based Recognition Method for Fault Diagnosis of Wind Turbines with Time-Domain Analysis and Multisensor Data Fusion
by: Xiafei Long, et al.
Published: (2019-01-01)