Ultra-Low-Energy Open-Circuit Fault Diagnosis for Three-Phase Inverters
TL;DR
This work proposes an event-driven neuromorphic framework for energy-efficient open-circuit (OC) fault diagnosis and achieves 11 microjoules per diagnosis, corresponding to a 382 times inference-energy reduction compared with a GPU-based CNN, while maintaining 100% diagnostic accuracy.
Abstract
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.