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.     

一种基于虚拟页地址映射的NAND Flash管理算法

本文提出了一种基于虚拟页地址映射的NAND Flash管理算法.该算法通过定义坏块表、对应表等结构，以及设计的坏块替换策略和虚拟页地址到实际物理页地址的转换算法，实现上层软件采用虚拟地址对NAND Flash的无坏块连续页地址访问.该算法是一种高效的地址映射算法，能高效地对数据进行索引，占用SRAM空间较少，使系统达到高性能，并使得闪存使用的更加稳定持久.     

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。     

电泵起停控制与电机多功能保护电路

某些场合，电泵要在特殊的环境和条件下长期连续的工作，因此配置特殊的起停控制电路和保护电路是完全必要的，所以设计了本文介绍的电泵起停控制与多功能保护电路。电路主要由集成件构成。文中扼要介绍的是电泵欠载延时断电保护，过载分级延时断电保护电路，以及欠载停机后延时自动再起动电路。     

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.     

基于XML的产品主模型技术研究

多学科设计优化方法是近年来发展的一种设计复杂系统的新方法。它充分考虑各个学科之间的相互影响和耦合作用,以获得系统的整体最优解。产品主模型技术是多学科设计优化的关键技术之一,能较好地解决应用模型之间的数据交换和通信。文章介绍了产品主模型的概念及其特点,对产品主模型技术及其实施过程进行了深入研究。回顾了可扩展标记语言的产生和发展历史,并对其特性进行了较详细介绍;研究了基于可扩展标记语言技术实现系统集成的技术路线;最后,提出了基于可扩展标记语言技术和产品主模型技术的卫星总体方案多学科设计优化集成框架。     

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。