将物理规律融入神经网络，提升变压器健康监测的可靠性与可信度。
Physics-Informed Machine Learning for Transformer Condition Monitoring -- Part II: Physics-Informed Neural Networks and Uncertainty Quantification
- 用物理约束增强神经网络，实现温度场与绝缘老化建模。
- 引入贝叶斯框架，量化不确定性，支持数据稀疏下的稳健预测。
- 适合电力系统故障诊断与智能运维研究者参考。
将物理知识与机器学习模型结合，正日益推动电气变压器的健康监测、故障诊断与寿命预测。本文为系列第二篇，聚焦于在学习过程中融合物理规律与不确定性量化。首先介绍物理信息神经网络（PINNs）的基本原理，应用于时空温度建模及固体绝缘材料老化分析。在此基础上，提出贝叶斯物理信息神经网络，作为量化认知不确定性、在数据稀疏条件下提供可靠预测的系统性框架。最后，展望了面向关键电力资产的感知物理与可信赖机器学习的前沿方向。
原文摘要 · Abstract (English)
The integration of physics-based knowledge with machine learning models is increasingly shaping the monitoring, diagnostics, and prognostics of electrical transformers. In this two-part series, the first paper introduced the foundations of Neural Networks (NNs) and their variants for health assessment tasks. This second paper focuses on integrating physics and uncertainty into the learning process. We begin with the fundamentals of Physics-Informed Neural Networks (PINNs), applied to spatiotemporal thermal modeling and solid insulation ageing. Building on this, we present Bayesian PINNs as a principled framework to quantify epistemic uncertainty and deliver robust predictions under sparse data. Finally, we outline emerging research directions that highlight the potential of physics-aware and trustworthy machine learning for critical power assets.
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