Semi-Supervised Adversarial Transfer Networks for Cross-Domain Intelligent Fault Diagnosis of Rolling Bearings
In recent advances, deep learning-based methods have been broadly applied in fault diagnosis, while most existing studies assume that source domain and target domain data follow the same distribution. As differences in operating conditions lead to the deterioration of diagnosis performance, domain a...
Main Authors: | Baisong Pan, Wuyan Wang, Juan Wen, Yifan Li |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-02-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/13/4/2626 |
Similar Items
-
An Intelligent Fault Diagnosis for Rolling Bearing Based on Adversarial Semi-Supervised Method
by: Yongchao Zhang, et al.
Published: (2020-01-01) -
Fault Diagnosis of the Rolling Bearing by a Multi-Task Deep Learning Method Based on a Classifier Generative Adversarial Network
by: Zhunan Shen, et al.
Published: (2024-02-01) -
The Method of Rolling Bearing Fault Diagnosis Based on Multi-Domain Supervised Learning of Convolution Neural Network
by: Xuejun Liu, et al.
Published: (2022-06-01) -
Cross-Machine Fault Diagnosis with Semi-Supervised Discriminative Adversarial Domain Adaptation
by: Xiaodong Wang, et al.
Published: (2020-07-01) -
A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis
by: Zhigang Zhang, et al.
Published: (2024-10-01)