论文标题
增强图像缺陷：飞机空气数据传感器的数据驱动故障检测方法
Augmented Imagefication: A Data-driven Fault Detection Method for Aircraft Air Data Sensors
论文作者
论文摘要
在本文中，提出了一种新型的数据驱动方法，称为“增强图像缺陷”，用于飞机空气数据传感器（AD）的故障检测（FD）。典范飞机空气数据传感器的FD问题，开发了基于深神经网络（DNN）的边缘设备上的在线FD方案。首先，将飞机惯性参考单元测量作为等效输入，可扩展到不同的飞机/飞行案件。收集了与6种不同的飞机/飞行条件相关的数据，以在培训/测试数据库中提供多样性（可伸缩性）。然后提出了基于DNN的飞行条件预测的增强图像缺乏。原始数据被重塑为用于卷积操作的灰度图像，并分析并指出了增强的必要性。讨论了不同种类的增强方法，即翻转，重复，瓷砖及其组合，结果表明，在图像矩阵的两个轴上的所有重复操作都会导致DNN的最佳性能。基于Grad-CAM研究了DNN的可解释性，这提供了更好的理解并进一步巩固DNN的鲁棒性。接下来，DNN型号是针对移动硬件部署进行了优化的带有增强图像缺陷数据的VGG-16。修剪DNN后，具有高精度（略微上升0.27％）的轻质模型（比原始VGG-16小98.79％），并获得了快速速度（时间延迟减少87.54％）。并实施了基于TPE的DNN的超参数优化，并确定了超参数的最佳组合（学习速率0.001，迭代时期600和批量100的量，在0.987中获得最高准确性）。最后，开发了基于边缘设备Jetson Nano的在线FD部署，并实现了飞机的实时监控。我们认为，这种方法是针对解决其他类似领域的FD问题的启发性的。
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.
