发表机构
TH Köln; Leiden University; Helmut Schmidt University; Toyota Racing（科隆应用科学大学; 莱顿大学; 赫尔穆特·施密特大学; 丰田赛车）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对传感器故障下虚拟传感鲁棒性不足的问题，提出多领域基准MuViS-C，涵盖十种故障模式，评测六种架构，发现梯度提升树最鲁棒，且鲁棒化策略可弥补注意力模型差距。
AI 中文摘要
虚拟传感，即从可用的传感器测量中估计难以测量的量，是网络物理系统中控制与监测的关键使能技术。然而，当传感器发生故障时，基于学习的预测器可能产生物理上不合理的估计，进而传播为系统级故障。我们认为，实际部署要求鲁棒性，并引入了MuViS-C，这是首个针对基于学习的虚拟传感中常见传感器故障的鲁棒性多领域基准。基于现有的标称性能基准和既定的损坏分类法，它涵盖了十种传感器故障模式，从细微漂移到灾难性信号丢失，并具有多种严重程度。这些故障模式与互补的鲁棒性度量配对，捕获损坏下的平均误差、相对退化以及最坏情况脆弱性。在来自六个领域的九个数据集上，我们对六种架构进行了基准测试，涵盖梯度提升树以及序列建模的主要归纳偏置：卷积、循环、注意力和MLP混合。在基于注意力的架构上，我们进一步探究了三种鲁棒化策略。我们发现：(i) 每个模型在损坏下都显著退化，至少在一种损坏设置下变得比朴素预测器更差；(ii) 梯度提升树集成实现了强鲁棒性；(iii) 专门的鲁棒化缩小了基于注意力的架构与最鲁棒模型之间的差距，尽管每种策略都损害了标称性能。该基准的多领域设计被证明至关重要，因为模型排名在不同数据集间发生变化，且没有单一领域能捕捉完整的鲁棒性图景。MuViS-C是开源的，并可扩展到新的数据集、故障模式、度量标准和模型。
英文摘要
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 covers ten sensor failure modes, from subtle drifts to catastrophic signal dropouts, at multiple severities. These are paired with complementary robustness measures capturing average error under corruption, relative degradation, and worst-case fragility. Across nine datasets from six domains, we benchmark six architectures spanning gradient-boosted trees and the major inductive biases for sequence modeling: convolution, recurrence, attention, and MLP-mixing. On the attention-based architecture, we further probe three robustification strategies. We find that (i) every model degrades substantially under corruption, becoming worse than a naïve predictor on at least one corruption setting, (ii) gradient-boosted tree ensembles achieve strong robustness, and (iii) dedicated robustification closes the gap between the attention-based architecture and the most robust models, though each strategy hurts nominal performance. The benchmark's multi-domain design proves essential, as model rankings shift across datasets, and no single domain captures the full robustness picture. MuViS-C is open-source and extensible to new datasets, failure modes, measures, and models.
