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.     

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。     

天线组阵中SUMPLE算法的合成相位漂移补偿

针对SUMPLE算法在低信噪比时存在的相位漂移问题,在分析权值估计误差引起的合成信号相位漂移现象的基础上,提出一种以固定时刻合成权值作为参考的补偿算法。该算法以收敛后某个固定时刻的合成权值作为参考,其他时刻的相位补偿值通过与该参考权值进行相关计算得到。从理论上分析了该算法的可行性,并进行仿真验证。理论分析与仿真结果表明,所给出的相位补偿方法能有效地消除合成信号的相位漂移现象,补偿后的合成信号相位中心稳定。     

深空通信中继文件传输协议设计

深空通信极远的传输距离导致链路误码率高,使文件的可靠传输需要较多的重传次数,而其传播时延长的特点,进一步加大了文件传输时延。研究以CCSDS(空间数据系统咨询委员会)文件传输协议的存储-转发机制,提出一种R-CFDP(中继型CFDP协议),设计了3种中继"存储-转发"策略,并数学建模推导了R-CFDP协议的文件传输时延理论计算式。仿真证明R-CFDP协议相对于CFDP(CCSDS文件传输协议)协议在传输时延方面具有优势。     

GF(q)域上的低密度校验(LDPC)码的译码及其在深空通信中的应用

低密度校验码是一类能有效逼近香农限的好码,而高进制的LDPC码具有比二进制LDPC码更好的性能,但其译码复杂度太高不利于工程应用。本文提出了一种基于协同优化算法的低复杂度的高进制LDPC码的译码算法,并讨论了其在深空通信中的应用。     

月球卫星精密定轨

随着空间应用需求的日益增大,深空探测已成为现实,而月球显然是人类走向深空的首选目标。发射月球探测器通常分3个阶段,其运动状态分别对应3种不同类型的轨道:近地停泊轨道、地月转移轨道和绕月轨道。月球是1个慢自转天体且无大气,就轨道解而言这些因素导致环月卫星的运动与地球卫星有所差别。本文针对月球探测任务的特点,从月球与地球的差别入手,在仔细分析月球卫星的受力状况前提下,着重阐述月球探测器在环月段精密定轨的方法原理和具体实现过程。     

深空测控通信技术发展趋势分析

为满足21世纪我国深空探测任务的需要，参考国外深空测控通信系统的先进技术，归纳和总结了未来相关技术发展的7个趋势，为我国深空测控通信技术的发展提供一些参考。