2021-07-06文献阅读

1 A multi-source dense adaptation adversarial network for fault diagnosis of machinery

创新点：
(1) 多源域域适应
(2) the dense convolution and fusion convolution blocks are used for deep feature extraction and fusion
(3) a joint loss function is reconstructed under the framework of unsupervised learning, which considers the distribution differences of the features and the label information simultaneously.

Multi-source Unsupervised Domain Adaptation for Machinery Fault Diagnosis under Different Working Conditions

创新点：
(1) in the first stage, multiple specific feature spaces are obtained, then the distributions of each pair of source and target domain are aligned since it is difficult to extract the common domain-invariant features for all domains
(2) in the second stage, by considering the domain specific decision boundaries, the probabilistic outputs of classifiers are also aligned

3 Multi-source transfer learning network to complement knowledge for intelligent diagnosis of machines with unseen faults

4 A Domain Adaptation with Semantic Clustering (DASC) method for fault diagnosis of rotating machinery

创新点： semantic clustering可以参考，考虑代码实现

5 Weighted domain adaptation networks for machinery fault diagnosis

创新点：多源域考虑加权