深度学习检测航空航天电力系统故障
Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems
DeepSeek团队用深度学习解决了400Hz航空航天电力系统故障检测，模型在边缘设备上运行速度快准确率高。
该研究提出了一种硬件感知深度学习框架，用于400Hz航空航天电力系统的多类电气故障和电能质量扰动检测。研究基于波音787电气架构建立高保真仿真模型，生成21种正常、扰动、切换、开路和短路条件下的电压电流波形。
研究比较了1D和2D卷积神经网络、长短期记忆网络等多种模型，紧凑型ResNet在175,685参数下达到96.94%软件测试准确率。
8位量化后在Xilinx Zynq芯片上实现95.87%准确率和6.
90毫秒延迟。
Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems
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.
This report is categorized under AITOP's "Papers" desk, focusing on China's AI industry and domestic foundation model ecosystem. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include ResNet, deep learning, power systems.
Source attribution: curated from arXiv cs.LG (@Ian C. Guzmán, Radu Babiceanu, Berker Peköz).
