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• W4386572723 endingPage "110958" @default.
• W4386572723 startingPage "110958" @default.
• W4386572723 abstract "Traditional deep learning fails to identify new faults when the number of faulty samples is limited. Existing meta-learning studies on cross-domain small-sample fault diagnosis do not fully account for the differences in the distribution of faults across domains between training data and new fault classes, which limits further performance improvement of the diagnosis model. In this study, we propose a new joint transfer fine-grained metric network for cross-domain few-shot fault diagnosis. First, a hybrid attention is used to enhance the feature extraction ability of the model and suppress redundant features. The proposed joint transfer function is used to align the corresponding subdomains of the source and target domains in small samples. Finally, cross-domain few-shot fault diagnostics is implemented using a modified fine-grained metric network. The proposed joint transfer fine-grained metric network outperformed other common meta-learning fault diagnosis methods on three mechanical device datasets." @default.
• W4386572723 created "2023-09-10" @default.
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• W4386572723 creator A5044470498 @default.
• W4386572723 date "2023-11-01" @default.
• W4386572723 modified "2023-10-09" @default.
• W4386572723 title "Novel joint transfer fine-grained metric network for cross-domain few-shot fault diagnosis" @default.
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• W4386572723 doi "https://doi.org/10.1016/j.knosys.2023.110958" @default.
• W4386572723 hasPublicationYear "2023" @default.
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