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Life sciences · Preprint
arXiv · September 9, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a simulation-based feasibility study demonstrating that a compact ResNet deep learning model can detect 21 classes of electrical faults and power quality disturbances in a 400 Hz aerospace power system with 96.94% software accuracy and 95.87% accuracy after 8-bit quantization on embedded hardware. The work establishes computational and hardware-level feasibility for edge AI deployment but lacks experimental validation on real aircraft electrical systems, limiting inference to proof-of-concept.
Simulation-based computational study with model comparison and embedded hardware deployment. Simulated electrical conditions in a 400 Hz aerospace power system (21 normal, disturbance, switching, open-circuit, and short-circuit conditions); no real aircraft or experimental systems.. Intervention: Compact ResNet deep learning model with 175,685 parameters for multiclass fault and disturbance detection. Compared with: Five alternative architectures (1D CNN, 2D CNN, LSTM, CNN-LSTM hybrid, MobileNet, VGG) evaluated under same training conditions. n = 147,000.
Compact ResNet achieved 96.94 percent software test accuracy on simulated 400 Hz aerospace power data After 8-bit quantization and deployment on Xilinx Zynq UltraScale Plus MPSoC, accuracy was 95.87 percent Model achieved mean neural-network accelerator latency of 6.90 ms per input record
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This is simulation-based feasibility work for an embedded AI system without experimental validation on real aircraft electrical systems, representing early-phase proof-of-concept rather than clinical or clinical-grade evidence.
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More 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. A high-fidelity simulation model inspired by the Boeing 787 electrical architecture generates voltage and current waveforms for 21 normal, disturbance, switching, open-circuit, and short-circuit conditions. Two datasets, each containing 73,500 samples, are formed from one-dimensional time-series signals and short-time Fourier transform time-frequency representations. Signal-processing augmentation, domain randomization, and class-specific generative adversarial networks increase waveform diversity, and the time-series dataset is released through IEEE DataPort. We compare 1D and 2D convolutional neural networks, long short-term memory networks, CNN-LSTM hybrids, ResNet, MobileNet, and VGG models under common training conditions. A compact ResNet provides the best accuracy-complexity tradeoff, achieving 96.94 percent software test accuracy with 175,685 parameters. After 8-bit quantization and deployment on a Xilinx Zynq UltraScale Plus MPSoC ZCU102, the model achieves 95.87 percent accuracy and a measured mean neural-network accelerator latency of 6.90 ms per input record. The results establish simulation-based, accelerator-level feasibility for embedded edge AI in aircraft electrical health monitoring and motivate future end-to-end data acquisition and experimental validation.
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