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.     

星载激光通信终端在轨机动对温度影响

对激光通信终端在轨瞬态温度变化开展了仿真，以期研究在轨机动的影响、热分析和热试验时机动模式的简化模拟。通过合理地分析与简化，建立终端的轨道热分析模型，准确模拟在轨机动，根据典型机动模式、外热流和涂层退化等因素设计了计算工况，得出了终端在轨运行过程中的温度场随时间和姿态变化规律。结果表明：不同机动模式下的温度变化存在差异，最大可达23.0℃，采用固定姿态或一维转动的简化热分析或热试验不能准确模拟实际飞行温度，甚至不能部分替代二维转动热分析或热试验；光学天线和反射镜的温度控制是制约终端工作的瓶颈，为终端设计合适的避光机动策略，可大幅度提高温度稳定度和均匀度。研究结果可以为光机电设备在轨机动策略的设计和改进提供参考。     

面向快速响应产品的星上目标定位及识别方法

针对遥感卫星星上目标的定位及识别，本文面向快速响应应用需求，首先对星上目标定位及识别工作模式进行研究，总结出狭义和广义两种模式，其次重点对像方-物方坐标双向定位算法进行梳理，并针对典型地物目标星上自动识别算法提出研究思路，最后给出算法的应用方法流程。本文研究为快速响应产品星上智能处理提供方法参考。     

Substitution of satellite-based land surface temperature defective data using GSP method

Land surface temperature (LST) as an important environmental variable provides valuable information for earth environmental system modelling. Currently, LST is obtained through satellite thermal sensors at various spatial and temporal resolutions. Although spatially continuous satellite-based LST measurements are intended to overcome the shortcomings of sparse ground-based LST measurements, LST images often contain anomalous values due to the existence of clouds or sensor malfunctioning. The problem becomes more serious where the users deal with high spatial resolution characterized by low temporal resolution. This study examines the capability of a newly developed graph signal processing (GSP) method using two-dimensional single-date thermal data. For this purpose, four Landsat/TIRS datasets are analyzed. The data of five elliptical regions on thermal images are eliminated and then reconstructed through the GSP method and using the LST values of the enclosing rectangles containing the ellipsoids. The results indicate that the temperature variation determined by the GSP method generally conforms to the original image LST values. According to a correlation test conducted on the original image LST and those obtained through the GSP method, the values vary from 58% to 95%, which is an above-the-average rate (RMSE from 0.69 to 2.27). The statistical analysis of the original image LST in both the elliptical regions and the enclosing rectangles containing the ellipsoids indicates that an increase in the variance of LST data causes an increased error in the calculation of temperature by the GSP method, and vice versa. The results of the analysis of variance (ANOVA) and Duncan test indicated that an increase in the number of the non-zero spectral bins would result in increased RMSE values for all the dates and the regions. Moreover, the model errors were significant at the 0.05 level across all the image date and five elliptical study regions. Based on the results, the use of this method is recommended for the reconstruction of LST missing values, where dissimilarity of atmospheric conditions limits the use of other methods that depend on the time series data of various dates and a great deal of data calculation.     

一种针对局部结构的有限元模型修正方法

针对实际结构有限元模型（FEM）的建模误差通常仅存在于局部区域，提出了一种对局部结构单独进行模型修正的方法。首先，根据频响函数（FRF）解耦理论得到由残余结构频响函数与包含待修正参数的局部结构动刚度所重构的整体结构频响函数的拟合值，然后通过迭代优化使其与测量值的残差最小化，从而得到参数的极大似然估计。在此基础上，将残差关于参数的灵敏度以局部结构动力学矩阵表示，建立了模型修正的基本方程，利用整体结构的测试数据即可直接对分离出来的局部结构进行模型修正。最后，对喷气式飞机和三角机翼飞机分别进行了数值模拟和实验研究，验证了所提方法的可行性和有效性。结果表明，所提方法可以成功地用于仅局部区域含有建模误差的实际结构有限元模型的修正，修正后的有限元模型的动态特性与实际结构有较好的一致性。     

一种高动态低信噪比卫星导航信号捕获方法

为提升高动态低信噪比环境下卫星导航信号的捕获性能,提出了一种基于分数阶傅里叶变换(FrFT)及部分匹配滤波(PMF)的捕获方法。在该方法中,接收机首先利用PMF对接收信号做分段相干积分,随后借助快速傅里叶变换(FFT)对分段积分结果做离散快速FrFT,最后通过检测FrFT输出的峰值完成信号的捕获。由于具有多普勒频率变化率的卫星导航信号在FrFT后呈现能量聚焦特性,所提方法能够显著提高信号的长时间相干积分增益。同时对所提算法的捕获概率、平均捕获时间以及算法复杂度等性能指标进行了理论分析及计算机仿真验证。仿真表明,与传统的PMF-FFT方法相比,所提方法能够通过延长相干积分时间的方式有效提升高动态低信噪比卫星导航信号的捕获概率、降低捕获时间。     

刹车装置性能试验台系统设计

主要介绍了刹车装置性能试验台的实现机理、采用的方案,有助于增进对液压试验台设计和可编程控制器PLC应用方面的了解。     

战术导弹折叠翼快速展开性能的测试技术

介绍某些战术导弹折叠翼快速展开特性测试技术。详细地介绍了测量方法、项目、数据处理和主要结果。试验是在北京空气动力研究所一座低速风洞中进行的。试验结果表明，获得的数据具有很高精度和可靠性，重复性也是令人满意的。