用脉冲神经网络实现低功耗电力转换器健康监测，精度提升且能耗降低270倍。
Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
- 分拆脉冲时序处理与物理约束，用可微分微分方程求解器训练
- 在含电磁干扰的同步降压转换器上，电阻误差从25.8%降至10.2%
- 支持持续状态追踪与事件驱动故障检测，适合部署在神经形态芯片上
始终在线的转换器健康监测需要亚毫瓦级边缘推理，而传统基于GPU的物理信息神经网络无法满足。本文将脉冲时间处理与物理约束分离：三层漏电积分-放电脉冲神经网络（SNN）估计无源元件参数，同时通过可微分常微分方程（ODE）求解器解耦展开的脉冲循环与物理损失，实现物理一致性训练。在受电磁干扰的同步降压转换器基准测试中，该SNN将等效电阻误差从25.8%降低至10.2%，优于无源元件±10%的制造容差，在神经形态硬件上预计能耗降低约270倍。持久膜电位状态进一步支持退化追踪和事件驱动故障检测，突发故障时脉冲率提升5.5个百分点。93%的脉冲稀疏性使其适用于Intel Loihi 2或BrainChip Akida等平台的始终在线部署。
原文摘要 · Abstract (English)
Always-on converter health monitoring demands sub-mW edge inference, a regime inaccessible to GPU-based physics-informed neural networks. This work separates spiking temporal processing from physics enforcement: a three-layer leaky integrate-and-fire SNN estimates passive component parameters while a differentiable ODE solver provides physics-consistent training by decoupling the ODE physics loss from the unrolled spiking loop. On an EMI-corrupted synchronous buck converter benchmark, the SNN reduces lumped resistance error from $25.8\%$ to $10.2\%$ versus a feedforward baseline, within the $\pm 10\%$ manufacturing tolerance of passive components, at a projected ${\sim}270\times$ energy reduction on neuromorphic hardware. Persistent membrane states further enable degradation tracking and event-driven fault detection via a $+5.5$ percentage-point spike-rate jump at abrupt faults. With $93\%$ spike sparsity, the architecture is suited for always-on deployment on Intel Loihi 2 or BrainChip Akida.
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