Optimal transport strategy-based meta-attention network for fault diagnosis of rotating machinery with zero sample
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10489-024-05524-9.pdf
Reference59 articles.
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2. Hou YD, Ma JL, Wang JJ, Li TZ, Chen ZQ (2023) Enhanced generative adversarial networks for bearing imbalanced fault diagnosis of rotating machinery. Appl Intell 53(21):25201–15
3. Meng Z, He HH, Cao W, Li JM, Cao LX, Fan JJ, Zhu M, Fan FJ (2023) A novel generation network using feature fusion and guided adversarial learning for fault diagnosis of rotating machinery. Expert Syst Appl 234:121058
4. Zhang ZQ, Zhou FN, Karimi HR, Fujita H, Hu X, Wen CL, Wang TZ (2023) Attention gate guided multiscale recursive fusion strategy for deep neural network-based fault diagnosis. Eng Appl Artif Intel 126:107052
5. Lu N, Cui ZY, Hu HY, Yin T (2023) Multi-view and Multi-level network for fault diagnosis accommodating feature transferability. Expert Syst Appl 213:119057
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