Research on electromagnetic compatibility analysis of automation equipment based on generative adversarial networks and pulse sparse convolution

Abstract

Electromagnetic interference (EMI) analysis in high-speed industrial systems is increasingly challenged by multi-gigahertz sampling rates, complex transient behaviors, and stringent real-time constraints. To address these challenges, this paper proposes a pulse-aware generative and analysis framework based on a generative adversarial network (GAN) combined with pulse sparse convolution using leaky integrate-and-fire (LIF) spiking neurons. A multi-scale discriminator and gradient penalty stabilization are employed to improve waveform generation fidelity, achieving a Fréchet distance (FID) of 0.72 and a global difference metric (GDM) of 0.18 ± 0.03 on an industrial-grade Electromagnetic compatibility (EMC) dataset. The proposed framework is further applied to crosstalk prediction, where it reduces pulse-width and phase prediction errors by more than 40% compared with classical numerical solvers such as finite-difference time-domain (FDTD), finite element method (FEM), and method of moments (MoM), and consistently outperforms representative learning-based EMC models. To enable real-time deployment, the pulse sparse convolution architecture is implemented on an field-programmable gate array (FPGA) platform using fixed-point arithmetic, achieving deterministic inference at 5 GS/s with a measured power consumption of 0.71 W. Extensive experiments on traction systems, industrial robots, CNC drives, photovoltaic inverters, and UAV (Unmanned Aerial Vehicle) electronics demonstrate that the proposed approach provides accurate, stable, and energy-efficient EMI analysis suitable for practical industrial EMC applications.