Dual prototypical contrastive network: a novel self-supervised method for cross-domain few-shot fault diagnosis
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DOI: 10.1007/s10845-023-02237-7
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- Xin Zhang & Haifeng Wang & Bo Wu & Quan Zhou & Youmin Hu, 2023. "A novel data-driven method based on sample reliability assessment and improved CNN for machinery fault diagnosis with non-ideal data," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2449-2462, June.
- Ke Zhao & Hongkai Jiang & Zhenghong Wu & Tengfei Lu, 2022. "A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 151-165, January.
- Cuixia Jiang & Hao Chen & Qifa Xu & Xiangxiang Wang, 2023. "Few-shot fault diagnosis of rotating machinery with two-branch prototypical networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1667-1681, April.
- Ruohui Hu & Min Zhang & Zaiyu Xiang & Jiliang Mo, 2023. "Guided deep subdomain adaptation network for fault diagnosis of different types of rolling bearings," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2225-2240, June.
- Vikas Singh & Purushottam Gangsar & Rajkumar Porwal & A. Atulkar, 2023. "Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 931-960, March.
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Keywords
Fault diagnosis; Few-shot learning; Self-supervised learning; Contrastive learning;All these keywords.
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