MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
Bearing fault diagnosis in actual working conditions often faces the problem that unknown type faults cannot be identified, which seriously restricts the practical application of fault diagnosis technology. To solve this problem, this paper proposes a bearing fault diagnosis method based on transfer...
Saved in:
Main Authors: | Guangyuan Xu, Ruifeng Guo, Zhenyu Yin, Feiqing Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/15/2/921 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Rolling Bearing Fault Diagnosis Based on a Synchrosqueezing Wavelet Transform and a Transfer Residual Convolutional Neural Network
by: Zihao Zhai, et al.
Published: (2025-01-01) -
A Target Domain-Specific Classifier Weight Partial Transfer Adversarial Network for Bearing Fault Diagnosis
by: Yin Bai, et al.
Published: (2025-01-01) -
Towards a Standard Benchmarking Framework for Domain Adaptation in Intelligent Fault Diagnosis
by: Mohammed M. Farag
Published: (2025-01-01) -
A study on rolling bearing fault diagnosis using RIME-VMD
by: Zhenrong Ma, et al.
Published: (2025-02-01) -
A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching
by: Chengyao Liu, et al.
Published: (2024-01-01)