Author

Siyu Liu

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Jul 2026

MAML-S3M: Selective state space meta-learning for cross-condition few-shot bearing fault diagnosis

A novel model-agnostic meta-learning framework based on a selective state space model (MAML-S3M) to address the challenge of cross-condition few-shot bearing fault diagnosis and achieves superior diagnostic accuracy, outperforming state-of-the-art methods by at least 1.1%.

Siyu Liu, Nan Wang, Xue-Yi Li et al. · Structural Health Monitoring · 0 citations