Deep multi-scale adversarial network with attention: A novel domain adaptation method for intelligent fault diagnosis

2021 ◽  
Vol 59 ◽  
pp. 565-576
Author(s):  
Bo Zhao ◽  
Xianmin Zhang ◽  
Zhenhui Zhan ◽  
Qiqiang Wu
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Intelligent Fault Diagnosis ◽  
Multi Scale ◽  
Adversarial Network ◽  
Adaptation Method

Intelligent Fault Diagnosis With Deep Adversarial Domain Adaptation

2021 ◽  
Vol 70 ◽  
pp. 1-9
Author(s):  
Yu Wang ◽  
Xiaojie Sun ◽  
Jie Li ◽  
Ying Yang
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Intelligent Fault Diagnosis

Double-level adversarial domain adaptation network for intelligent fault diagnosis

2020 ◽  
Vol 205 ◽  
pp. 106236 ◽  
Author(s):  
Jinyang Jiao ◽  
Jing Lin ◽  
Ming Zhao ◽  
Kaixuan Liang
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Intelligent Fault Diagnosis

scholarly journals Spectral Normalization for Domain Adaptation

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 68
Author(s):  
Liquan Zhao ◽  
Yan Liu
Keyword(s):  
Transfer Learning ◽  
Predictive Accuracy ◽  
Domain Adaptation ◽  
Learning Performance ◽  
Constraint Condition ◽  
Adversarial Network ◽  
Network Methods ◽  
The Stability ◽  
Spectral Normalization ◽  
Adaptation Method

The transfer learning method is used to extend our existing model to more difficult scenarios, thereby accelerating the training process and improving learning performance. The conditional adversarial domain adaptation method proposed in 2018 is a particular type of transfer learning. It uses the domain discriminator to identify which images the extracted features belong to. The features are obtained from the feature extraction network. The stability of the domain discriminator directly affects the classification accuracy. Here, we propose a new algorithm to improve the predictive accuracy. First, we introduce the Lipschitz constraint condition into domain adaptation. If the constraint condition can be satisfied, the method will be stable. Second, we analyze how to make the gradient satisfy the condition, thereby deducing the modified gradient via the spectrum regularization method. The modified gradient is then used to update the parameter matrix. The proposed method is compared to the ResNet-50, deep adaptation network, domain adversarial neural network, joint adaptation network, and conditional domain adversarial network methods using the datasets that are found in Office-31, ImageCLEF-DA, and Office-Home. The simulations demonstrate that the proposed method has a better performance than other methods with respect to accuracy.

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