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。     

逆向工程的关键技术及其研究

综述了逆向工程中实物数字化技术、测量数据的预处理、曲面重建技术、重构曲面的评价和检验等关键技术的发展现状和存在的问题,并对今后的发展方向进行了讨论。     

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。     

飞机外形件三角网格模型光滑B样条曲面重建

综合考虑曲面重建时的拟合精度、曲面片拼接处的连续性和曲面的光顺性等因素,以三角网格模型的双三次B样条曲面重建为例,研究了飞机外形件三角网格模型的分片光滑B样条曲面重建方法。该方法首先采用最小二乘法逼近散乱数据,初步获得分片B样条曲面;然后将光顺准则加权到最小二乘函数中调节曲面光顺程度;最后运用罚函数法保证曲面片边界近似G1连续。将该方法应用于飞机外形件实测数据的曲面重建,误差分析结果验证了方法的有效性。     

基于径向基函数神经网络的自由曲面重构

根据径向基函数神经网络（RBFNN）具有很强的非线性逼近能力的优点，本文采用RBF网络模型进行自由曲面重构，建立了适应于曲面重构的径向基函数网络模型，讨论了基函数对重构曲面连续性的影响，并与多自由曲面重构，建立了适应于曲面重构的径向基函数风络模型，讨论了基函数对重构曲面连续性的影响，并与多层感知器神经网络的性能进行对比。理论分析和仿真实验结果表明：常用的几种径向基函数重构的曲面都具有很好的连续性，径向基函数网络用于曲面重构，不论是在拟合精度，还是网络的训练速度都明显优于多层感知器网络，具有一定的实用价值。