Supervision-Guided Contrastive Learning for Rolling Bearing Fault Diagnosis Under Label Scarcity
While deep learning has advanced industrial fault diagnosis, its reliance on massive labeled datasets remains a major obstacle to real-world application. In response to the scarcity of labeled fault samples and ineffective utilization of abundant unlabeled data, this paper proposes a novel semi-supe...
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| Автори: | , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
IEEE
2026-01-01
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| Серія: | IEEE Access |
| Предмети: | |
| Онлайн доступ: | https://ieeexplore.ieee.org/document/11540076/ |
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