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.
