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.     

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。     

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。     

费率受制于位相半马氏过程的风险模型

对带马氏链的线性和非线性系统，经过众多学者近20多年的努力，已取得一系列重要成果。然而，由于马氏链状态逗留时间服从负指数分布，使得这些成果在应用中有很大的局限性。本文首先探讨了位相半马氏过程的性质，然后以位相半马氏过程代替马氏过程，研究风险模型的相关问题，取得了与马氏链相同的研究成果，从而说明了位相半马氏过程在克服由马氏过程带来的局限性中的作用。     

一种划分航空知识点类型的方法

基于航空知识提出一种划分知识点类型的方法,对各种知识点类型进行统计,给出知识点的存储结构。     

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。     

应力极值分布、强度威布尔分布的可靠性

本文运用应力—强度干涉理论,推导了应力为Ⅰ型极小值分布,强度为威布尔分布的可靠度计算公式,并对冗长的计算公式进行简化,在简化公式的基础上,运用一定的数学技巧,改变积分公式中的积分变量和上下限。将被积函数化成在某一区域内的可积函数。采用de Boor编制的一种严谨的自适应Romberg外推格式的FORTRAN程序进行数值积分。对应予不同的组合参数,给出应力服从Ⅰ型极小值分布,强度服从威布尔分布的可靠度数值。本文最后讨论了服从这两种分布的组合参数的变化对可算度数值变化的影响。     

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.     

低特征信号绝热层用硅氧烷树脂研究

简述了用于固体火箭发动机低特征信号绝热层研制的发展现状。对多种硅氧烷树脂（ＲＴＶ）进行了筛选和信号透过率，热失重，力学及粘接笥能试验。试验结果表明，与使用ＥＰＤＭ绝热层材料相比，使用ＲＴＶ绝热层材料，火箭发动机燃烧室羽流具有较高的信号透过率；ＲＴＶ还具有较好的热性、力学及粘接性能，适于用作低特征信号绝热层的基体材料。