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。     

低频声波对水雾消散作用的实验研究

作者考查了低频声波(<50Hz)对于水雾消散的作用。通过建立声波消雾实验装置,测定了在水雾消散过程中,雾室内的声场分布、温度、消雾时间、雾滴谱的变化过程。实验结果表明声波作用对于水雾的消散具有明显的作用。并且,消雾的效果随着作用声波的频率和声压级(SPL)呈规律性的变化趋势。较低频率和较高声强的声波作用更有利于水雾的消散,在消雾的过程中温度起伏不大。而声致凝聚是声波消雾的主要因素之一。     

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.     

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。     

多普勒雷达导引头信号处理技术

概述防空导弹多普勒雷达导引头信号的基本形式、主要特点、检测原理及其滤波系统；介绍了多普勒雷达导引头信号处理的一些常用技术。     

基于AT89C52的离散信号测量电路设计

介绍了离散信号测量与控制在反舰导弹雷达导引头自动测控系统中的重要性 ,给出了基于AT89C5 2单片机控制的离散信号测量电路的具体构成和工作原理