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。     

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。     

移动机器人铣削制孔系统基准检测

结合航空大部件的数字化对接装配需求,设计了一套用于对接面铣削制孔的移动机器人加工系统,并提出具体的加工工艺流程。研究了基于激光轮廓扫描仪的大量散乱点云数据的预处理算法,提出基于栅格法和迭代拟合法的对接面特征提取算法。针对移动机器人加工系统在大场景下的高精度定位问题,提出基于扫描线法和最小二乘法原理的对接基准孔坐标找正算法。通过产品的加工实验验证,对接面铣削和制孔精度满足系统要求的各项技术指标,证明了本文提出的移动机器人铣削制孔系统的装配对接面加工方法能够精确地完成大部件数字化装配任务,对提高装配质量和效率具有重要意义。     

中华世纪坛旋转噪音测试方法的研究

着重阐述了中华世纪坛在实际工作中为解决噪音问题所采用的测试方法，寻求一种解决长时间、多数据测试分析的途径。     

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。     

有线电视网络安全监控系统

在有线电视台前端采用数字信标插入器在视频插入标志,在有线电视网中对此标志进行检查,即可判断有线网络中播出的信号是否合法,若不符合设定,则通过电话网络向前端发出报警信号,前端收到报警信号后,向值班人员发出声光报警,从而达到对有线电视网络进行实时监控的目的。较详细地说明了该系统及各主要部分的原理及具体实现。     

CC—高阶通信系统的并发演算

并发演算CC(Concurrent Calculus)是高阶并发通信系统的数学模型,它把λ-演算作为子理论并包含一阶通信系统演算CCS、活动进程演算CMP、和高阶通信系统演算CHOCS的主要特征。在CC中,通信端口可为任意表达式并且进程和通信端口都可以作为在通信中传递的一等对象(First-class Objects)。从而CC不仅可以描述一阶通信行为而且可以刻划通信网络的动态自修改行为。另外,由于CC把λ-演算和进程演算纳入同一形式系统,故CC可以作为并发函数式程序设计的核心语言和理论模型。本文首先给出CC的语法、语义和例子,然后研究CC的高阶双向模拟等价关系以及CC的代数定律。最后简单讨论了相关的工作和今后的研究方向。     

一个串行口Javabean的研究与设计

本文描述了一个基于JavaBean组件标准的串行通信组件的实现原理、实现过程以及实现方法。它将被作为C／S系统的一个组，以实现某些工业控制场合中远程控制的要求。充分利用了Java语言的事件监听机制、串行性、持久性、自检以及JavaBean的定制等特性，并经由Java本地接口（JNI－Java Native Interface)调用硬件控制等底层操作，具体实现了一个串行通信组件必需的功能，如初始化、读写操作等。在所需要的.DLL的支持下，所有安装了Java虚拟机的Windows系统都可以使用这个.Jar文件。