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Reliable Virtual Sensing: A Multi-Domain Benchmark for Robustness Under Sensor Failures
Virtual sensing, the estimation of hard-to-measure quantities from available sensor measurements, is a critical enabler for control and monitoring in cyber-physical systems. However, when sensors fail, learning-based predictors can produce physically implausible estimates that propagate to system-level failures. We argue that real-world deployment demands robustness and introduce MuViS-C, the first multi-domain benchmark of robustness against common sensor failures in learning-based virtual sensing. Building on an existing nominal-performance benchmark and established corruption taxonomies, it
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T09:52:49.000Z
First collected: 2026-09-20T08:01:03.945Z. This is not the publication date.