Bearing Fault Diagnosis Based on Multilayer Domain Adaptation
Bearing fault diagnosis plays a vitally important role in practical industrial scenarios. Deep learning-based fault diagnosis methods are usually performed on the hypothesis that the training set and test set obey the same probability distribution, which is hard to satisfy under the actual working c...
Saved in:
Main Authors: | Bingru Yang, Qi Li, Liang Chen, Changqing Shen |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2020-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2020/8873960 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research on unsupervised domain adaptive bearing fault diagnosis method
by: WU ShengKai, et al.
Published: (2024-06-01) -
Adaptive Morphological Feature Extraction and Support Vector Regressive Classification for Bearing Fault Diagnosis
by: Jun Shuai, et al.
Published: (2017-01-01) -
Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning
by: Jing An, et al.
Published: (2020-01-01) -
Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions
by: Zhe Tong, et al.
Published: (2018-01-01) -
ROLLING BEARING FAULT DIAGNOSIS BASED ON ADAPTIVE RCGMVMFE AND MANIFOLD LEARNING
by: LIU WuQiang, et al.
Published: (2022-01-01)