Cargando…
Feature Enhancement Method of Rolling Bearing Based on K-Adaptive VMD and RBF-Fuzzy Entropy
The complex and harsh working environment of rolling bearings cause the fault characteristics in vibration signal contaminated by the noise, which make fault diagnosis difficult. In this paper, a feature enhancement method of rolling bearing signal based on variational mode decomposition with K dete...
Autores principales: | Jiao, Jing, Yue, Jianhai, Pei, Di |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871395/ https://www.ncbi.nlm.nih.gov/pubmed/35205492 http://dx.doi.org/10.3390/e24020197 |
Ejemplares similares
-
Adaptive VMD–K-SVD-Based Rolling Bearing Fault Signal Enhancement Study
por: Mao, Meijiao, et al.
Publicado: (2023) -
The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy
por: Zhuang, Deyu, et al.
Publicado: (2023) -
Rolling Bearing Fault Diagnosis Based on WGWOA-VMD-SVM
por: Zhou, Junbo, et al.
Publicado: (2022) -
GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction
por: Ding, Jiakai, et al.
Publicado: (2020) -
A Rolling Bearing Fault Feature Extraction Algorithm Based on IPOA-VMD and MOMEDA
por: Yi, Kang, et al.
Publicado: (2023)