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.     

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.     

FET振荡器注入锁相伏特拉级数分析

应用伏特拉级数法分析了电容耦合ＦＥＴ振荡系统在外加信号下的注入锁定现象，推导出输出信号和注入锁定带宽表达式。与一般非线性系统分析方法相比，伏特拉级数法在建立好系统模型后只需进行代数运算，从而避开了求解非线性，非齐次高阶微分方程的复杂过程。     

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。     

八丝热线探针制作及其应用

介绍了八丝热线探针的制作工艺,有效地消除了各热丝探头之间的电信号干扰和动力学干扰。实验中使用该探针检测到了湍流边界层内的大尺度旋涡,并测量了它们的强度和尺度。与此同时还获得了速度、湍流强度和功率谱等流场参数,结果与现有典型数据一致。说明本文所述的多丝热线探针具有很强的实用性。     

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。     

逆合成孔径雷达信号处理方法研究进展

在简要评述国内外现有的逆合成孔径雷达（ISAR）信号处理的运动补偿和成象方法之后，介绍了南京航空航天大学ISAR课题组近年来在ISAR信号处理方法研究中取得的主要进展，包括基于多散射点定位观点的雷达成象观测模型，基于最大似然原理的同时运动补偿和成象方法，基于最大似然原理的多目标成象方法和基于时频分析的多目标成象方法，以及超分辩成象方法，文末指出了在ISAR信号处理方面有待进一步研究的问题。     

基于信号采样的干扰信号产生技术

目前电子对抗系统广泛使用的数字储频技术主要是通过采样、存储和复制产生干扰信号.阐述了基于采样技术的信号重构及其基本原理,重点介绍分析了在此基础上利用信号处理技术,合成产生干扰信号的原理和方法.     

伪码调相与PAM复合测距系统研究

介绍了PAM伪随机码的构成原理,推导了PAM伪随机码的自相关函数和功率谱密度。结合图形分析了它们的性质特点。设计了伪码调相与PAM复合测距系统,并分析了在加性高斯白噪声信道中的各个阶段的信号形式。研究了复合测距系统的性能,详细推导了复合测距系统的信噪比增益。结果表明,与单纯的伪码测距系统相比,复合测距系统有更强的抗干扰性能和测距能力。     

大型自动化系统的供电与接地

本文是一篇关于运载火箭自动化系统和生产自动化系统的研制经验的总结报告。“供电与接地”的设计质量,不但影响系统的抗干扰特性,而且直接危及设备和人身的安全。本文概要性地论述了系统及其组成设备的供电与接地类型,供电与接地的正确方法,供电与接地在不同研制阶段中常见的错误,地线系统的设计、施工与测量方法等。