Machines’ Intelligent Fault Diagnosis Based on Hierarchical Refined Composite Generalized Multiscale Fluctuation Dispersion Entropy
Vibration data from mechanical equipment contain extensive information distributed across multiple dimensions. Single-scale analysis fails to comprehensively reflect its damage characteristics, thereby reducing fault diagnosis accuracy. This study proposes a novel signal vibration feature extraction...
Saved in:
Main Authors: | Biwen Chen, Changsheng Chen, Zhenlai Ma, Guoping Li, Yi Zhang, Baoyue Li |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2024-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2024/2235272 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A New Method of Fault Feature Extraction Based on Hierarchical Dispersion Entropy
by: Peng Chen, et al.
Published: (2021-01-01) -
RESEARCH ON FAULT DIAGNOSIS METHOD OF ROTATING MACHINERY BASED ON REFINED IMPROVED MULTISCALE FAST SAMPLE ENTROPY (MT)
by: ZHOU FuMing, et al.
Published: (2023-01-01) -
Fault Diagnosis of Gearbox Multi-channel Vibration Signal based on Improved Multivariate Multiscale Dispersion Entropy
by: Fuming Zhou, et al.
Published: (2021-04-01) -
An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering
by: Hongping Ge, et al.
Published: (2022-01-01) -
Feature Extraction of Ship-Radiated Noise Based on Hierarchical Dispersion Entropy
by: Leilei Xiao
Published: (2022-01-01)