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Robust multimodal latent repair for fault-tolerant cyber–physical production systems
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DOI：10.1016/j.jmsy.2026.04.030.png)
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Abstract
En 中文
• We derive spectral proofs of fault diffusion and anchoring in Transformer blocks. • A novel Residual-Contrastive Attention module isolates industrial sensor faults. • The Local–Global Ratio enables precise, unsupervised latent anomaly detection. • We achieve state-of-the-art correction across three heterogeneous robotic datasets.
Keywords:
Multimodal learning
Robust sensor fusion
Self-attention mechanisms
Fault-tolerance
Industrial cyber–physical systems
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