Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

In aerospace industry, gears are the most common parts of a mechanical transmission system. Gear pitting faults could cause the transmission system to crash and give rise to safety disaster. It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder (ADSAE) is proposed. The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data. This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear. The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions. The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy. This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults. The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods. This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.     

深空探测器推进系统

以技术较成效的月球探测器、火星探测器推进系统为重点，介绍了国外深空探测活动的情况。简述了深空探测对推进系统的需求、我国航天器液体推进技术的现状。认为我国的推进技术经一些适应性发展，即可满足深空探测的需求，并且设想了我国近期较为可行的深空探测器推进系统方案。     

Expectations for Infrared Spectroscopy of 9P/Tempel 1 from Deep Impact

The science payload on the Deep Impact mission includes a 1.05–4.8 μm infrared spectrometer with a spectral resolution ranging from R∼200–900. The Deep Impact IR spectrometer was designed to optimize, within engineering and cost constraints, observations of the dust, gas, and nucleus of 9P/Tempel 1. The wavelength range includes absorption and emission features from ices, silicates, organics, and many gases that are known to be, or anticipated to be, present on comets. The expected data will provide measurements at previously unseen spatial resolution before, during, and after our cratering experiment at the comet 9P/Tempel 1. This article explores the unique aspects of the Deep Impact IR spectrometer experiment, presents a range of expectations for spectral data of 9P/Tempel 1, and summarizes the specific science objectives at each phase of the mission.     

Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

Unmanned Aerial Vehicles (UAVs) are useful in dangerous and dynamic tasks such as search-and-rescue, forest surveillance, and anti-terrorist operations. These tasks can be solved better through the collaboration of multiple UAVs under human supervision. However, it is still difficult for human to monitor, understand, predict and control the behaviors of the UAVs due to the task complexity as well as the black-box machine learning and planning algorithms being used. In this paper, the coactive design method is adopted to analyze the cognitive capabilities required for the tasks and design the interdependencies among the heterogeneous teammates of UAVs or human for coherent collaboration. Then, an agent-based task planner is proposed to automatically decompose a complex task into a sequence of explainable subtasks under constrains of resources, execution time, social rules and costs. Besides, a deep reinforcement learning approach is designed for the UAVs to learn optimal policies of a flocking behavior and a path planner that are easy for the human operator to understand and control. Finally, a mixed-initiative action selection mechanism is used to evaluate the learned policies as well as the human’s decisions. Experimental results demonstrate the effectiveness of the proposed methods.     

基于高光谱和LiDAR的黄河口湿地植被分类方法

利用无人机（Unmanned Aerial Vehicle, UAV）高光谱影像（Hyper-spectral Imaging, HSI）和激光雷达（Light Detection and Ranging, LiDAR）数据开展黄河口湿地植被分类方法研究。由于高空间分辨率HSI光谱变异性强,以及LiDAR点云密度不均匀,分类结果呈现出“椒盐”现象。为了解决这些问题,本文提出了一种结合空谱特征融合和通道注意力机制的双分支卷积神经网络（SSF-C-DBCNN）。光谱注意力机制通过为每个波段分配不同的权重来减少光谱变异性的影响。空间注意力机制侧重于学习和强调特征表达能力强的密集点云区域空间信息,从而减轻LiDAR点云密度不均匀对结果的影响。最后,在双分支融合特征后引入通道注意力机制来提取更深层次的特征。利用UAV采集的HSI和LiDAR数据进行实验验证,结果表明,本文提出方法的性能优于随机森林和五种深度学习方法,分类结果更为贴合实际土地覆盖,有效地抑制了“椒盐”现象。     

深空探测器运行轨道的基本性态

要成功地发射一个深空探测器进入目标轨道,相应的运行过程基本上涉及3个阶段:近地停泊轨道运行段、转移轨道的过渡段和进入目标天体的绕飞段。它们各自的运行状态和相应的数学模型有所差别,特别是转移轨道段的运行特征与绕飞段的卫星轨道的典型特征之间的重大差别,在深空探测任务中受到广泛的重视,如平动点利用中的晕(Halo)轨道和引力加速的节能过渡等。然而,就太阳系而言,这些不同轨道之间有一共同的基本性态,那就是都可以用在牛顿万有引力定律制约下的开普勒轨道(或变化的开普勒轨道)来刻画。本文将针对上述不同运行段轨道对应的数学模型、研究方法和结果,结合我们所做的工作进行综述。     

天线组阵技术研究及其在我国深空测控通信系统中的应用

研究深空通信系统中天线组阵技术实现方案,并通过数值仿真对几种基本的天线组阵方案和算法的性能进行比较分析。仿真结果表明,采用SUMPLE算法的全频谱合成技术能够获得很好的合成效率,是适合我国深空探测工程测控通信系统设计的有效方案,有望在我国后续的深空探测工程中得到应用。     

月球与深空探测频率规划研究

介绍国际电（ITU）和空间频率协调组（SFGC）关于月球与深空探测频率使用的一些规定和建议,研究国外月球与深空探测频率使用规划和发展趋势,探讨我国月球与深空探测频率使用需求,提出我国月球与深空探测频率建议和发展方向。     

深空探测器无线电干涉测量开源数据处理系统构建及试验

为更好地开展深空无线电干涉测量试验,基于DiFX(Distributed FX,分布式FX型相关处理)、SPICE(Spacecraft Planet Instrument Camera-matrix Events,航天器行星仪器照相机矩阵事件)、HOPS(Haystack Observatory Postprocessing System,Haystack克天文台后处理系统)及AIPS(Astronomical Image Processing System,天文图像处理系统)等开源软件,搭建了一套可用于深空探测器信号相关处理的无线电干涉测量数据处理系统。首先介绍了该系统的整体架构及组成,然后介绍了系统各组成模块的功能及工作原理,最后利用该系统对嫦娥三号着陆器开展了观测试验,并对观测数据进行了处理。试验结果表明,该系统能成功实现深空航天器信号的相关处理、条纹拟合,以及时延解算等功能,有望为我国嫦娥五号任务及火星探测任务数据处理提供支持。     

表面处理对聚酰亚胺柔性复合材料粘结性能的影响

使用聚酰亚胺(PI)膜和PI纤维编织布制备深空探测用柔性织物复合材料,研究表面处理对PI膜和PI纤维编织布之间粘结性能的影响。采用自制表面处理剂分别对PI膜和PI纤维编织布的表面进行处理,再经硅橡胶胶黏剂粘结制备柔性复合材料。使用T剥离强度试验方法测试柔性织物复合材料的层间胶接性能,并分析复合材料剥离面的形貌状态。结果显示,PI膜和PI织物的表面处理可以显著提高柔性织物复合材料的T剥离强度。其中,PI膜和PI织物未经表面处理时,柔性织物复合材料的T剥离强度为8.9 N/cm。对PI膜进行表面处理,或者对PI膜和PI织物均进行表面处理的情况下,柔性织物复合材料的T剥离强度增加至11.7 N/cm和12.8 N/cm,分别提高了31.5%和43.8%。这表明对PI膜及PI织物进行合理的表面处理,可以显著提高柔性织物复合材料的胶接性能。