Vibration Images-Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise
Deep learning-based fault diagnosis of rolling bearings is a hot research topic, and a rapid and accurate diagnosis is important. In this paper, aiming at the vibration image samples of rolling bearing affected by strong noise, the convolutional neural network- (CNN-) and transfer learning- (TL-) ba...
Saved in:
Main Authors: | Hongwei Fan, Ceyi Xue, Xuhui Zhang, Xiangang Cao, Shuoqi Gao, Sijie Shao |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2021-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2021/6616592 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN
by: CHEN DaiJun, et al.
Published: (2023-12-01) -
FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNN
by: LI SiQi, et al.
Published: (2020-01-01) -
ROLLING BEARING FAULT DIAGNOSIS BASED ON FUSION CNN AND PSO-SVM
by: WANG YongDing, et al.
Published: (2021-01-01) -
A VMD and CNN Combined Fault Diagnosis Method for Rolling Bearings
by: Li Kui, et al.
Published: (2022-11-01) -
RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON MA OPTIMIZATION OF CNN
by: TIAN LiYong, et al.
Published: (2024-08-01)