A new -means singular value decomposition method based on self-adaptive matching pursuit and its application in fault diagnosis of rolling bearing weak fault
Sparse decomposition has excellent adaptability and high flexibility in describing arbitrary complex signals based on redundant and over-complete dictionary, thus having the advantage of being free from the limitations of traditional signal processing methods such as wavelet and fast Fourier transfo...
Saved in:
Main Authors: | Hongchao Wang, Wenliao Du |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2020-05-01
|
Series: | International Journal of Distributed Sensor Networks |
Online Access: | https://doi.org/10.1177/1550147720920781 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Feature Extraction of Weak-Bearing Faults Based on Laplace Wavelet and Orthogonal Matching Pursuit
by: Lei Hou, et al.
Published: (2022-01-01) -
Weak Feature Extraction of Rolling Bearing Fault Based on Generalized Variational Mode Decomposition
by: Guo Yanfei, et al.
Published: (2023-05-01) -
Bearing Fault Diagnosis based on Singular Value Decomposition Denoising and Local Characteristic-scale Decomposition
by: Cui Weicheng, et al.
Published: (2016-01-01) -
BEARING FAULT DIAGNOSIS METHOD BASED ON WINGER DISTRIBUTION AND SINGULAR VALUE DECOMPOSITION
by: QIN HongMao, et al.
Published: (2015-01-01) -
Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing
by: Zhi Xu, et al.
Published: (2020-01-01)