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.     

一种软件脆弱性自动分析定位的方法

摘要： 为了更好地满足航空航天领域中使用的多种嵌入式软件的高可靠性要求，设计一种软件脆弱性自动分析定位的方法.该方法基于程序切片技术，改进现有的前向计算动态切片算法，利用动态程序切片收集程序运行时的动态信息，构造程序切片谱，设定相关统计量统计程序语句的脆弱性可疑度，生成脆弱性定位报告.在发现软件中存在的脆弱性后，该方法对导致此脆弱性的程序代码根源实现了自动分析定位.基于此方法设计开发了软件脆弱性自动定位工具并进行了实例验证分析，实验证明了该方法的有效性.     

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。     

现场动平衡技术在生产中的应用

利用美国恩泰克公司提供的DP1500数据采集仪及现场动平衡软件,以实例说明了现场动平衡技术在锅炉引风机、排粉风机、电动机、机泵等转动设备不平衡故障排除中的应用,介绍了不平衡故障的识别方法,揭示出该技术在生产中的实用性、先进性和经济性.     

飞机主操纵系统疲劳试验载荷谱小操纵位移频次问题研究

针对飞机全机主操纵系统疲劳试验载荷谱中小操纵位移频次影响问题进行了分析和讨论,确定了某机全机主操纵系统小操纵位移频次的级别。同时,在原某机全机主操纵系统疲劳试验载荷谱的基础上再次对其小操纵位移频次进行了计算,去掉了这一级别以下对全机主操纵系统疲劳试验无影响的小操纵位移频次,从而完善了主操纵系统疲劳试验载荷谱。另外,对小操纵位移频次问题研究结果在其全机主操纵系统疲劳试验中的实施效果进行了阐述。     

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。     

多重直扩信号的快速捕获

多重直扩信号的快速捕获得卫星定位及通信系统研制过程中遇到的一个主要课题，它的实现，不仅可以提高系统的多址能力，而且也是系统实现的必备条件，本文阐明了多重直扩信号的来源以及可被分离的理论依据，给出了多重直扩信号快速捕获的硬件实现方案及部分实验结果，得出了一些重要结论。     

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。