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。     

卫星定位导航系统中各因素对D-R定位算法精度的影响

D- R定位算法在理论上可以用于定位。在实际工作中 ,用户的定位精度是进行定位时首先需要考虑的问题。文中主要讨论 D- R算法本身固有的定位误差。首先根据 D- R算法的定位公式推导出算法的精度误差因子。然后根据卫星定位导航系统的工作过程 ,确定了影响用户定位精度误差因子的因素并对这些因素对精度误差因子的影响进行了计算机仿真。仿真结果证明 D- R定位算法在一定条件下可以满足用户对定位精度的要求 ,用户精度误差因子可与现有的 GPS系统相当。     

一种基于移步预测消除脉冲串扰的快速算法

以毫米波主被动复合探测器为工程应用背景,通过分析信号的特点,提出一种基于移步预测思想的快速算法来消除脉冲调制信号对被动通道信号的串扰。仿真分析和实验结果表明,该方法对改善信噪比效果明显,而且具有成本低、速度快和易于工程化实现的优点。     

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。     

RTM成型工艺技术进展

文章就当前RTM的发展趋势、机械设备、原材料和模具等进行综述。     

基于DWDM光接入网的波长分配算法

本文介绍了一种基于DWDM光接入网的波长分配算法，该算法在保证基本服务质量的前提下，通过调度共享波长降低业务请求过程中的阻塞率。文中还对该算法进行了计算机仿真实验，仿真结果表明了该算法的有效性。     

捷联惯导系统的圆锥误差补偿算法研究

非互易向量的确定与补偿是影响高动态、恶劣振动环境下捷联惯性导航系统姿态矩阵计算的重要问题。为了补偿由此引起的圆锥误差，本文对一些传统算法进行了分析和研究。由于以往各种算法一般都需要输入信息为角度信息，但对于高动态、恶劣振动环境，角速度信息更有工程意义，所以针对输入信息为角速度的情形，提出了一类新的姿态算法，通过对比传统的算法，新算法在同样计算量、存储量的情况下有较优的性能，算法的精度较传统的算法高。另外，对新的姿态算法进行了误差分析．为其参数选择提供了选择标准，有工程实用价值。