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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| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
IEEE
2026-01-01
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| Series: | IEEE Access |
| Assuntos: | |
| Acceso en liña: | https://ieeexplore.ieee.org/document/11540076/ |
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