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The results show that standard virtual-sensing models exhibit substantial tail degradation hidden by global MAE, that existing DIR methods can improve balanced performance but transfer unevenly to multimodal time-series data, and that tail-region instability remains a largely hidden failure mode under current DIR evalu...
It is argued that real-world deployment demands robustness and introduced MuViS-C, the first multi-domain benchmark of robustness against common sensor failures in learning-based virtual sensing, which covers ten sensor failure modes, from subtle drifts to catastrophic signal dropouts, at multiple severities.
This work proposes a self-supervised masking scheme that simulates common sensor failures and explicitly trains the model to recover the original signal, and demonstrates that the resulting representations significantly improve the robustness of predictions to seen and unseen sensor failures on a vehicle dynamics datas...
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