[论文] Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems (arXiv:2609.10479)

论文概要

研究领域: ML 作者: Ian C. Guzmán, Radu Babiceanu, Berker Peköz 发布时间: 2026-09-09 arXiv: 2609.10479

中文摘要

多电飞机需要快速可靠地监控高频电气网络，但大多数电能质量扰动和故障诊断方法针对传统50或60Hz电网开发。本文提出一种硬件感知的深度学习框架，用于400Hz航空电力系统中电气故障和电能质量扰动的多类检测。受波音787电气架构启发的高保真仿真模型为21种正常、扰动、开关、开路和短路条件生成电压和电流波形。紧凑的ResNet在软件测试中达到96.94%准确率（175,685参数），在Xilinx Zynq UltraScale Plus MPSoC ZCU102上8位量化部署后达到95.87%准确率和6.90ms平均加速器延迟。

原文摘要

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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*自动采集于 2026-09-11*

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