Self-Adaptation Graph Attention Network via Meta-Learning for Machinery Fault Diagnosis With Few Labeled Data

Effective application of fault diagnosis models requires that new fault types can be recognized rapidly after they occur few times, even only one time. To this end, a self-adaptation graph attention network via meta-learning (SGANM) is proposed. Specifically, based on a collected large-scale labeled...

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