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.     

相关函数在涡流检测信号提取分析中的应用

为降低涡流检测信号处理的硬件复杂程度及提升后续数据分析处理灵活度，在不借助复杂硬件自平衡技术电路前提下，在LABVIEW图形化软件平台中设计了相关函数算法程序，对示波器MDO4024C所采集的原始交流电桥信号及电桥激励信号分别进行自相关及互相关运算去噪，提取分析不锈钢管不同位置的涡流信号的幅值及相位，最后再将检测信号在极坐标图中分析显示。实验结果表明，这一方法可有效实现对不锈钢管缺陷涡流信号的去噪及特征信号提取。     

一种高动态低信噪比卫星导航信号捕获方法

为提升高动态低信噪比环境下卫星导航信号的捕获性能,提出了一种基于分数阶傅里叶变换(FrFT)及部分匹配滤波(PMF)的捕获方法。在该方法中,接收机首先利用PMF对接收信号做分段相干积分,随后借助快速傅里叶变换(FFT)对分段积分结果做离散快速FrFT,最后通过检测FrFT输出的峰值完成信号的捕获。由于具有多普勒频率变化率的卫星导航信号在FrFT后呈现能量聚焦特性,所提方法能够显著提高信号的长时间相干积分增益。同时对所提算法的捕获概率、平均捕获时间以及算法复杂度等性能指标进行了理论分析及计算机仿真验证。仿真表明,与传统的PMF-FFT方法相比,所提方法能够通过延长相干积分时间的方式有效提升高动态低信噪比卫星导航信号的捕获概率、降低捕获时间。     

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。     

扩压器-水泵内非稳定流动研究

水泵内部流动实质上是复杂的三维非稳定流动 ,它对水泵性能及结构振动有重要影响。本文介绍了一种求解这种复杂内流动的数值方法。三维雷诺数平均的纳维斯托克斯方程 ( 3-DReynolds-averaged Navier-Stokes,RANS)以及标准 k-ε的方程用于描述水泵内非定常紊流流场。系统特性方程与水泵的 CFD模型相结合以考虑流体在管道中的加速 ;采用任意滑移网格界面模拟叶轮和静止部件之间的相互干涉 ;将整个叶轮作为分析对象 ,以考虑失速情况下流动的周向非对称。这些技术的结合包括了水泵内非稳定流动的物理实质。一台实验数据比较齐全的离心式 -扩压器水泵被用于验证所提出的数值方法     

模拟磁带重放信号数字化过程中的频偏校正技术及应用

模拟磁带重放信号中存在的频率偏移会对信号的处理分析产生不利影响。利用模拟磁记录器的多个磁道同时记录的能力,将实际信号和特定的参考信号同时进行记录,磁带信号重放和数字化过程中,利用这个参考信号来控制A/D采集时钟的速度,从而可在很大程度上减少磁带重放系统带来的频率偏移。实验结果表明,该技术方法能有效地解决模拟磁带重放信号中存在的频偏问题。     

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。     

一种可抑制三次谐波的五相混合式步进电机空间矢量脉宽调制优化控制策略

五相混合式步进电机因其定位精度高、转矩脉动小以及转矩密度高等优点而应用愈加广泛。为进一步减小其运行噪声、提高系统整体效率,本文提出一种可抑制三次谐波的五相混合式步进电机空间矢量脉宽调制(Space vector pulse width modulation,SVPWM)优化控制策略。首先搭建了考虑互感条件下的五相混合式步进电机解耦控制数学模型。在此基础上,采用相邻两大矢量和两中矢量合成混合电压矢量的SVPWM控制策略,并给出了相应的五相混合式步进电机双闭环控制系统。本文所提的混合SVPWM算法通过利用大、中矢量在基波、三次谐波双坐标系对应关系以达到抑制三次谐波的效果,能够有效降低相电流纹波和谐波含量。最后,研制了一套基于FPGA的实验平台,对所提策略与传统滞环算法、大矢量SVPWM算法进行对比研究,重点分析了3种算法对相电流纹波和谐波含量的影响,从而验证了所提算法的正确性和优越性。     

正—反激组合式变换器与单端式变换器的比较

以５０Ｗ变换器为例，分别从磁性元件的大小，功率管上的电应力、磁恢复问题，输出纹波及输出特性等方面，详细分析比较了组合式变换器与单端式变换器的优缺点，并给出了具体数据，提出了正－反激组合式变换器具有电压应力低，磁性元件小，体积小，重量轻，输出纹波小及可靠性高的结论。