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

Abstract

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 parameters such as the damping coefficient. These parameters are difficult to measure directly, and existing methods based on empirical values are both time-consuming and often inaccurate. This paper proposes a method using Physics-Informed Neural Networks (PINNs) combined with experimental data to accurately invert damping parameters in vibration systems, exemplified by GIS. PINNs integrate physical laws into neural networks, improving accuracy, robustness, and generalization. By combining experimental data with PINNs, high precision and interpretability of key physical parameters in FEM simulations are achieved. The results show that without noise, the waveform similarity between the FEM and the experimental results is high, with an amplitude similarity coefficient of 0.869 and a normalized cross-correlation of 0.926. At the 1% noise level, the inversion error of the damping parameter is only 3%, and the method shows good noise resistance up to the 5% noise level. This approach improves simulation reliability and provides a new path to enhance the transparency and diagnostic capabilities of power equipment.

Keywords

• Parameter inversion
• Physics-informed neural networks(PINNs)
• Damping vibration system
• GIS fault diagnosis