Real-Time Partial Discharge Detection at the Edge Via Hybrid Time-Frequency Sequence Modeling

Abstract

Reliable partial discharge detection is critical for ensuring the insulation integrity of automotive electrical machines. However, deploying highly accurate diagnostic algorithms on resource-constrained edge computing devices remains challenging due to severe industrial background noise. This paper proposes a lightweight hybrid diagnostic architecture that integrates physical signal processing with a data-driven sequence model. First, the stationary wavelet transform is utilized to explicitly decouple transient discharge energy from broadband noise, providing a robust physical prior. Subsequently, a gated recurrent unit network, enhanced by a numerically stabilized temporal attention mechanism, adaptively aggregates the isolated transient features across time steps. Experimental evaluations on a generalized industrial dataset demonstrate that the proposed framework achieves an overall accuracy of 99.78%, surpassing complex two-dimensional deep learning models while reducing the parameter footprint by $\mathbf{9 7. 0 \%}$. Furthermore, hardware-in-the-loop tests on an edge microcontroller validate a total end-to-end processing latency of 14.69 milliseconds. The results confirm that the proposed system strictly satisfies the sub-cycle latency requirements of continuous testing environments, effectively achieving high-fidelity electrical diagnosis under severe computational constraints.