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.     

基于CFD高精度算法的雷电电磁场研究

雷电空间电磁场一直是电磁领域研究的热门，成熟的算法例如有限时域差分法（FDTD），传输线矩阵法（TLM）以及频域的矩量法（MoM）在计算雷电问题方面都有广泛的应用。由于计算流体力学（CFD）中的Euler方程与电磁学中的Maxwell方程有着相同的守恒形式，而且采用间断伽辽金方法（DG）已经在流场问题上得到广泛的尝试，因此引入了基于计算流体力学的DG方法来离散时域Maxwell方程，并采用网格分区并行技术加速计算，使用基于DG的圆球雷达散射截面积（RCS）算例进行测试，数值结果一致表明DG算法在求解电磁场问题上的可行性，之后通过计算一段近场雷电通道的电场分布并与解析解、某算法仿真解对比，数据基本吻合，说明该方法适合于雷电电磁场的计算。     

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.     

基于北斗三号的长基线共视时间比对

卫星导航共视时间比对一直是远距离时间比对的重要方法之一。使用我国最新发射的北斗三号全球导航卫星，基于中科院国家授时中心（NTSC）和捷克光电研究院（TP）各自的时间产生和保持系统，开展了中捷北斗三号长基线共视时间比对试验。本文对中捷两站各自的北斗卫星可视数及其卫星高度角情况进行了统计分析，利用Vondark滤波对时间比对结果进行了降噪处理，最后将北斗三号共视时间比对结果与北斗二号及GPS共视时间比对结果进行了比较。结果表明：北斗三号在当前全球组网阶段中捷共视可视卫星数比北斗二号还少的情况下，其共视时间比对精度达到1.16ns，较北斗二号提升约19%，与GPS更为接近。     

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。     

高速低压差分信号（LVDS）器件应用设计方法研究

随着高速LVDS器件得到广泛应用，在高速信号使用LVDS传输时，信号传输线路问题、信号传输波形的最佳化成为非常重要的课题之一。由于印制电路板的布局布线直接影响信号传输质量，因此利用仿真对印制板分析成为设计上不可缺少的手段。有鉴于此，文章深入探讨了高速电路的设计，总结出设计技巧。     

原子时自动测量及数据采集系统

介绍了普通的时间实验室的时频测量,实现自动测量和数据收集的基本系统方案,并以上海天文台原子时实验室为例介绍了实际可行的自动测量和数据收集系统,这个系统高性能、低价格,可应用于国内几乎所有的时间实验室。     

弧焊电源动态特性测试方法的研究

本文对弧焊特性、特别是它的动特性进行了讨论，与过去传统的时域法不同，文中提出了用频域法分析弧焊的电源特性的设想，文内中方法的理论、实验方法及实测结果进行了讨论。     

VKM—5型立式捏合机研制与应用

介绍自行研制的VKM—5型立式捏合机的构造及工作原理,详述了该机采用液压传动和气动元件进行位置检测以及气、电、液安全联锁的特点,并说明与卧式捏合机对比试验情况。     

卡尔曼滤波动态优化及其在遥控机器人空间实验中应用

本文是在文献[1]的基础上,讨论遥控机器人追踪自由飞行目标物的动态性能问题。应用黄金分割法和元素交替极小化法对扩展的卡尔曼滤波初值条件进行搜索并获得优化解。仿真结果表明优化方法简单可行,优化解满足系统性能要求。