Virtual sensors use machine learning to predict target signals from available measurements, replacing expensive physical sensors in critical applications. Existing virtual sensor approaches require application-specific models with hand-selected inputs for each sensor, cannot leverage task synergies, and lack consistent benchmarks. At the same time, emerging time series foundation models are computationally expensive and limited to predicting their input signals, making them incompatible with virtual sensors. We introduce the first foundation model for virtual sensors addressing both limitations. Our unified model can simultaneously predict diverse virtual sensors exploiting synergies while maintaining computational efficiency. It learns relevant input signals for each virtual sensor, eliminating expert knowledge requirements while adding explainability. In our large-scale evaluation on a standard benchmark and an application-specific dataset with over 18 billion samples, our architecture achieves 415x reduction in computation time and 951x reduction in memory requirements, while maintaining or even improving predictive quality compared to baselines. Our model scales gracefully to hundreds of virtual sensors with nearly constant parameter count, enabling practical deployment in large-scale sensor networks.

翻译：虚拟传感器利用机器学习从可用测量数据中预测目标信号，在关键应用中替代昂贵的物理传感器。现有虚拟传感器方法需要针对每个传感器使用特定应用模型并人工选择输入信号，无法利用任务间的协同效应，且缺乏统一的基准测试体系。与此同时，新兴的时间序列基础模型存在计算成本高昂且仅限于预测其输入信号的局限，使其无法适用于虚拟传感器场景。我们提出了首个克服这两大局限的虚拟传感器基础模型。我们的统一模型能够同时预测多样化的虚拟传感器并利用其协同效应，同时保持计算效率。该模型可自动学习每个虚拟传感器的相关输入信号，在消除专家知识依赖的同时增强了可解释性。我们在包含180亿样本的标准基准测试和特定应用数据集上进行大规模评估，结果表明：相较于基线模型，我们的架构在保持甚至提升预测质量的同时，实现了计算时间降低415倍、内存需求减少951倍的显著改进。该模型能够以近乎恒定的参数量优雅扩展至数百个虚拟传感器，为大规模传感器网络的实际部署提供了可行性。