在现场泄漏诊断中用于电气建模的新范式:使用基于物理的神经网络进行标签稀缺的三维模拟替代品
Feng Chen1, Aixia Zhou2, Linhai Ye3
1The State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
Water research
|February 6, 2026
概括
这项研究引入了基于物理学的神经网络 (PINN),以更快地诊断电泄漏. PINN显著加快了3D电气建模的速度,克服了工业场所管理中的计算瓶.
科学领域:
- 地质物理学和电气工程
- 计算科学和机器学习
背景情况:
- 电气泄漏诊断对于工业场所管理至关重要.
- 传统的有限元法 (FEM) 前期建模需要大量的计算资源来完成校准和数据生成等任务,延迟了部署.
研究的目的:
- 开发一种新的物理信息神经网络 (PINN) 替代模型,用于高通量,无网格的3D电气建模.
- 通过克服FEM的计算局限性,加速电气泄漏诊断.
主要方法:
- 设计了一个PINN,将基于物理的约束纳入其损失函数.
- 采用准静态采样和内部H1规范化用于模型优化.
- 在一个有线设施的有限数据集 (48个样本) 上训练PINN.
主要成果:
- 在验证错误中实现了>90%的减少,平均绝对误差 (MAE) 为8.0×10−4和相对L2误差为0.2%.
- 通过PINN,PINN的计算速度是FEM的近1000倍,并且具有很高的准确性.
- 该模型提供连续和物理严格的电场预测.
结论:
- PINN框架为电气诊断中的前建模提供了一个新的范式.
- 这种方法显著减少了计算需求,并加速了泄漏检测的现场部署.
- 将下游工作流与FEM瓶脱,从而实现高效的监控.
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