三相逆变器的超低能量开路故障诊断
Ultra-Low-Energy Open-Circuit Fault Diagnosis for Three-Phase Inverters
AI总结：
研究三相逆变器超低能量开路故障诊断，提出事件驱动神经形态框架，将训练的CNN转换为SNN，利用轨迹矩阵稀疏结构节能，实验表明该方法诊断能量低、推理能量降382倍且准确率100%，还验证了鲁棒性。
AI中文摘要：
三相逆变器中的嵌入式故障诊断必须满足转换器控制硬件的亚瓦特功率预算，但传统的基于卷积神经网络（CNN）的方法需要密集的乘法累加运算，并产生大量的推理能量。这项工作提出了一种用于节能开路（OC）故障诊断的事件驱动神经形态框架。在电流矢量轨迹矩阵上训练的CNN被转换为脉冲神经网络（SNN），并使用基于Loihi的神经形态能量估计的NengoLoihi框架进行评估。通过利用轨迹矩阵的稀疏结构，SNN仅在信息区域激活计算，而不是密集地处理整个特征图。在三相逆变器平台上的实验表明，该方法每次诊断的能量为11微焦耳。与基于GPU的CNN相比，推理能量降低了382倍，同时保持了100%的诊断准确率。在不平衡负载、电流幅度阶跃变化和注入测量噪声的情况下，鲁棒性得到了进一步验证。
英文摘要：
Embedded fault diagnosis in three-phase inverters must satisfy the sub-watt power budget of converter control hardware, but conventional convolutional neural network (CNN)-based methods require dense multiply-accumulate operations and impose substantial inference energy. This work proposes an event-driven neuromorphic framework for energy-efficient open-circuit (OC) fault diagnosis. A CNN trained on current-vector trajectory matrices is converted into a spiking neural network (SNN) and evaluated using the NengoLoihi framework with Loihi-based neuromorphic energy estimation. By exploiting the sparse structure of trajectory matrices, the SNN activates computation only in informative regions instead of processing the full feature map densely. Experiments on a three-phase inverter platform show that the proposed method achieves 11 microjoules per diagnosis, corresponding to a 382 times inference-energy reduction compared with a GPU-based CNN, while maintaining 100% diagnostic accuracy. Robustness is further validated under unbalanced loading, current amplitude step changes, and injected measurement noise.
