A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features
The nonstationary components and noises contained in the bearing vibration signal make it particularly difficult to extract fault features, and minimum entropy deconvolution (MED), maximum correlated kurtosis deconvolution (MCKD), and fast spectral kurtosis (FSK) cannot achieve satisfactory results....
Main Authors: | ChengJiang Zhou, Jun Ma, Jiande Wu, Zezhong Feng |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2019-01-01
|
Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2019/7182539 |
Similar Items
-
MVMD-MOMEDA-TEO Model and Its Application in Feature Extraction for Rolling Bearings
by: Zhuorui Li, et al.
Published: (2019-03-01) -
Research on Fault Feature Extraction Method Based on FDM-RobustICA and MOMEDA
by: Jingzong Yang, et al.
Published: (2020-01-01) -
Feature extraction for rolling element bearing weak fault based on MOMEDA and ICEEMDAN
by: Lei Zhao, et al.
Published: (2018-09-01) -
An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform
by: Zezhong Feng, et al.
Published: (2019-02-01) -
An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy
by: Zhuorui Li, et al.
Published: (2021-01-01)