In this paper, a novel data-driven approach named Augmented Imagefication for Fault detection (FD) of aircraft air data sensors (ADS) is proposed. Exemplifying the FD problem of aircraft air data sensors, an online FD scheme on edge device based on deep neural network (DNN) is developed. First, the aircraft inertial reference unit measurements is adopted as equivalent inputs, which is scalable to different aircraft/flight cases. Data associated with 6 different aircraft/flight conditions are collected to provide diversity (scalability) in the training/testing database. Then Augmented Imagefication is proposed for the DNN-based prediction of flying conditions. The raw data are reshaped as a grayscale image for convolutional operation, and the necessity of augmentation is analyzed and pointed out. Different kinds of augmented method, i.e. Flip, Repeat, Tile and their combinations are discussed, the result shows that the All Repeat operation in both axes of image matrix leads to the best performance of DNN. The interpretability of DNN is studied based on Grad-CAM, which provide a better understanding and further solidifies the robustness of DNN. Next the DNN model, VGG-16 with augmented imagefication data is optimized for mobile hardware deployment. After pruning of DNN, a lightweight model (98.79% smaller than original VGG-16) with high accuracy (slightly up by 0.27%) and fast speed (time delay is reduced by 87.54%) is obtained. And the hyperparameters optimization of DNN based on TPE is implemented and the best combination of hyperparameters is determined (learning rate 0.001, iterative epochs 600, and batch size 100 yields the highest accuracy at 0.987). Finally, a online FD deployment based on edge device, Jetson Nano, is developed and the real time monitoring of aircraft is achieved. We believe that this method is instructive for addressing the FD problems in other similar fields.
翻译:在本文中,提出了一种新型数据驱动方法,名为 " 飞机空气数据传感器(ADS)失灵检测加速度(ADS)600(AGD) " 。例如飞机空气数据传感器(ADS)的FD问题,正在开发一个基于深神经网络(DNN)的边缘设备在线FD计划。首先,飞机惯性参考单位的测量作为等效输入被采用,可扩缩到不同的飞机/飞行案例。收集了与6个不同的飞机/飞行条件相关的数据,以提供培训/测试数据库中的多样性(可扩缩性)。然后,为基于DNNN的飞行条件预测而增加图像。原始数据被重塑为飞动操作的灰度图像,而增强增强的扩增能力。 不同的强化方法,即:Flip、Sold、Tile、Tile及其组合,结果显示,在图像矩阵中的“All重复”模型的组合可以达到DNFM的最佳性能。DNM的解读性能根据GAM模型研究D-98的精确度预测,这是更精确的硬的快速的硬性数据,在DNFDGOD-ral dreal dreal dreal dreal dreal dreal dreal dreald dreald disld disald disl disld,这是最接近的原始的更精确度,这是更深的硬的硬的硬的硬的模型,这是更深的硬的数据。