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ARC-GAN: An auditing and reconstructing framework for zero-shot bearing fault diagnosis
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DOI：10.1016/j.conengprac.2026.107089.png)
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Abstract
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• Proposed ARC-GAN, a novel generative framework for zero-shot fault diagnosis. • Introduced a dual-correction mechanism for content and structural consistency. • Designed a Semantic Auditor to align global manifold topology via metric learning. • Developed an Attribute Reconstructor to distill noise-invariant fault features. • Validated robustness against both signal SNR noise and attribute noise.
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