基于物理的图神经网络用于工业物联网中的软传感
Physics-Enhanced Graph Neural Networks For Soft Sensing in Industrial Internet of Things
AI总结：
本文提出基于物理的图神经网络，通过整合物理原理提升传感器数据建模的准确性，尤其在存在噪声和参数误差时表现更优。
摘要：
工业物联网（IIoT）正在重塑制造、工业过程和基础设施管理。通过促进新的自动化、效率和预测维护水平，IIoT正在将传统行业转变为智能、无缝连接的生态系统。然而，实现高度可靠的IIoT可能受到安装大量传感器成本、现有系统升级限制或恶劣环境条件等阻碍。软（虚拟）传感利用数学模型从物理传感器数据估计变量，提供了解决方案。数据驱动和基于物理的建模是软传感的两种主要方法。选择这些策略取决于底层系统的复杂性，数据驱动方法通常在物理推断模型复杂且状态估计有挑战时更受青睐。然而，传统深度学习模型通常受其无法显式表示各种传感器之间复杂相互作用的限制。为解决这一限制，我们采用图神经网络（GNNs），以其有效捕捉传感器测量之间复杂关系的能力而闻名。在本研究中，我们提出物理增强的GNNs，通过在输入图中添加额外节点来整合物理原理。我们的评估显示，在区域供暖网络案例研究中，该方法在存在噪声和参数误差时显著优于纯数据驱动的GNNs。
英文摘要：
The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. By fostering new levels of automation, efficiency, and predictive maintenance, IIoT is transforming traditional industries into intelligent, seamlessly interconnected ecosystems. However, achieving highly reliable IIoT can be hindered by factors such as the cost of installing large numbers of sensors, limitations in retrofitting existing systems with sensors, or harsh environmental conditions that may make sensor installation impractical. Soft (virtual) sensing leverages mathematical models to estimate variables from physical sensor data, offering a solution to these challenges. Data-driven and physics-based modeling are the two main methodologies widely used for soft sensing. The choice between these strategies depends on the complexity of the underlying system, with the data-driven approach often being preferred when the physics-based inference models are intricate and present challenges for state estimation. However, conventional deep learning models are typically hindered by their inability to explicitly represent the complex interactions among various sensors. To address this limitation, we adopt Graph Neural Networks (GNNs), renowned for their ability to effectively capture the complex relationships between sensor measurements. In this research, we propose physics-enhanced GNNs, which integrate principles of physics into graph-based methodologies. This is achieved by augmenting additional nodes in the input graph derived from the underlying characteristics of the physical processes. Our evaluation of the proposed methodology on the case study of district heating networks reveals significant improvements over purely data-driven GNNs, even in the presence of noise and parameter inaccuracies.
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