Intelligent Fault Diagnosis of Rolling Bearing Based on Gramian Angular Difference Field and Improved Dual Attention Residual Network
With the rapid development of smart manufacturing, data-driven deep learning (DL) methods are widely used for bearing fault diagnosis. Aiming at the problem of model training crashes when data are imbalanced and the difficulty of traditional signal analysis methods in effectively extracting fault fe...
Main Authors: | Anshi Tong, Jun Zhang, Liyang Xie |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-03-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/24/7/2156 |
Similar Items
-
Rolling bearing fault diagnosis based on Gramian angular difference field and improved channel attention model
by: Lunpan Wei, et al.
Published: (2024-01-01) -
Bearing fault diagnosis based on Gramian angular field and DenseNet
by: Yajing Zhou, et al.
Published: (2022-09-01) -
A Lightweight Model for Bearing Fault Diagnosis Based on Gramian Angular Field and Coordinate Attention
by: Jialiang Cui, et al.
Published: (2022-04-01) -
Non-technical losses detection with Gramian angular field and deep residual network
by: Yuhui Chen, et al.
Published: (2023-10-01) -
A New Method for Diagnosing Motor Bearing Faults Based on Gramian Angular Field Image Coding and Improved CNN-ELM
by: Yuhan Zhang, et al.
Published: (2023-01-01)