Physics-informed neural networks for robust equivalent damping parameter inversion and fault diagnosis in gas-insulated switchgear vibration systems

Physics-informed neural networks for robust equivalent damping parameter inversion and fault diagnosis in gas-insulated switchgear vibration systems

Abstract Accurate simulation of vibration signals is essential for fault detection in power equipment such as gas-insulated switchgear (GIS) and transformers. The Finite Element Method (FEM) is commonly employed for high-fidelity simulations, but its precision heavily depends on exact physical param...

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Bibliographic Details
Main Authors: Yuhang Li, Yuhui Lv, Yingjie Yan, Xiang Gao, Xiuchen Jiang, Yadong Liu
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Parameter inversion
Physics-informed neural networks(PINNs)
Damping vibration system
GIS fault diagnosis
Online Access:https://doi.org/10.1038/s41598-025-10148-1
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https://doi.org/10.1038/s41598-025-10148-1

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