Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems
Compact ResNet detects electrical faults in 400 Hz aircraft power systems with 95.87 percent accuracy after deployment on Xilinx Zynq MPSoC.
The study targets multiclass fault and power quality disturbance detection in 400 Hz aerospace networks using a high-fidelity simulation model inspired by the Boeing 787 electrical architecture, covering 21 normal, disturbance, switching, open-circuit, and short-circuit conditions. Two 73,500-sample datasets are built from 1D waveforms and STFT time-frequency representations, augmented with domain randomization and class-specific GANs, and the time-series dataset is released via IEEE DataPort. A compact ResNet with 175,685 parameters achieved 96.94 percent software test accuracy, and 95.87 percent after 8-bit quantization on a Xilinx Zynq UltraScale Plus ZCU102 with 6.90 ms mean accelerator latency per record.
- 21-condition 400 Hz aerospace fault dataset (73,500 samples) released via IEEE DataPort
- Compact ResNet: 96.94% software accuracy, 95.87% on ZCU102 after 8-bit quantization
- Measured 6.90 ms accelerator latency per record on Zynq MPSoC
From the original article
1 min read at arxiv.orgMore Electric Aircraft require fast and reliable monitoring of high-frequency electrical networks, yet most power quality disturbance and fault diagnosis methods are developed for conventional 50 or 60 Hz grids. This work presents a hardware-aware deep learning framework for multiclass detection of electrical faults and power quality disturbances in a 400 Hz aerospace power system.
By Ian C. Guzmán · arxiv.org
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