arXiv:2611.09141 · v1

Circuit Theory-Informed AI for Condition Monitoring in Power Electronics

首次提交 2026-11-06 16:16

版本更新 2026-11-06 16:16

雷达观测 2026-11-09 11:19

摘要

Artificial intelligence for power electronics is evolving toward a deeper comprehension of system intrinsic behaviors and dynamics. As a novel integration of neural computation and electric circuits, spike-based neuromorphic computing offers promising potential in high energy efficiency and adaptability for data-driven applications. Building on the latency-coding principles, this article proposes a framework for data-light condition monitoring that relies solely on three-phase AC current measurements. The AC current signals are encoded as membrane potentials of RC-based spiking neurons, which are inherently compatible with spiking neural networks that can effectively facilitate the estimation of device parameters. Case studies are presented to validate the performances, while constraints and further optimizations are also evaluated and discussed, and future extensions of the methodology are provided.

作者备注：The manuscript has been accepted for publication in the Proceedings of 2026 IEEE Design Methodologies Conference (DMC 2026)