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.     

PBT弹性体微相分离及对其力学性能的影响研究

为了获得微相分离对宽温PBT叠氮聚醚弹性体力学性能的影响规律,用红外光谱分析法(FT-IR)和动态热力学分析法(DMA)研究了PBT叠氮聚醚弹性体产生微相分离的机理和影响因素。用调节硬段含量、交联参数的方法调控其微相分离,控制适当的微相分离程度可显著改善推进剂的力学性能。研究结果表明:在二元醇扩链的弹性体体系中,当硬段含量约为15%时,弹性体发生相对最大比例的微相分离,体现出较佳的综合力学性能。通过微相分离的调控可获得宽温范围内综合力学性能较好的叠氮聚醚推进剂用粘合剂基体材料。     

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。     

平面形状误差分离方法的比较分析

就十种典型的精密平面形状误差测量－分离方法的权函数及其零点，作了分析比较。按形状误差谐波抑制最小准则，论证了五点式正交逐次三点法、九点式正交逐次三点法、复合三点法、不对称二维混合法和不对称四点法优于其他方法，不对称四点法则较为理想。     

高精度圆度仪误差分离装置研制及测量不确定度分析

介绍了一种由程控型多齿分度台和高精度圆度仪组合而成的全自动误差分离装置，该装置能够使圆度仪主轴回转误差从被测工件测量结果中可靠分离，从而极大地提高了圆度测量不确定度，本文对该装置测量不确定度进行了分析。     

以经营者人力资本效用价值为主导的激励模型研究

对经营者激励机制的研究在我国已有多年，但一直未形成良好的激励体系。本文提出的以经营者人力资本效用价值为主导的激励模型是在企业效益分离的基础上，以股票期权和股票期股的组合方式设计的激励模式。     

多点法测量形状误差的补偿控制系统

经过对误差分离技术理论进行了分析之后,又对误差补偿技术进行了研究,并建立了多点法测量形状误差的补偿控制系统。通过补偿主轴径向误差运动,提高了工件的圆度误差和圆柱度误差。     

液压油箱壳体大盲孔的光整加工技术

分析了液压油箱壳体大盲孔的加工难点，介绍了硬质阳极化后光整加工大盲孔的工艺方法及取得的技术经济效果。