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.     

基于DOA聚类算法的ARM抗有源诱偏技术研究

有源诱偏技术是雷达对抗反辐射导弹的一种重要手段,可以大大降低反辐射导弹的作战效能。为提高对敌目标的精确打击能力,在分析闪烁诱饵诱偏原理的基础上,针对有源诱偏干扰下被动雷达测角精度与稳定度不高的问题,通过对有源诱偏信号的时域特征进行分析,提出一种基于脉冲前沿检测的DOA聚类分选算法,找出前沿超前的辐射源信号,实现了高性能抗有源诱偏干扰和高精度抗干扰测向。仿真结果表明,该技术可以有效对抗四点源有源诱偏干扰。     

一种基于OPTICS聚类的仅测角导航目标检测算法

仅测角自主导航方法具有设备简单、复杂度低，功耗低的优点，在空间任务中具有广泛的应用前景.针对中远距离下空间目标特征少的特点，提出了一种利用基于OPTICS聚类算法的空间目标检测方法，可用于仅测角导航过程中的目标检测.对原始星图进行预处理提取星点及目标点，并结合星图识别的结果选择部分帧，使用经过改进的OPTICS聚类方法获得目标运动轨迹.最后，使用本文中的算法对软件仿真出的含有目标的高精度星图进行处理验证了算法的可行性.在卫星相对于空间目标抵近过程中，目标检测的水平误差及垂直误差小于0.15°的帧数分别占到了85.4%以及99.6%.相比AVANTI实验中的目标检测方法，减少了在轨任务中相关参数的调节，进一步提升了算法的自主性.     

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.     

基于相对最值点的最小二乘直线检测方法

为了解决传统目标检测中遇到的检测速度慢、精度低等问题，本文提出一种新的图像特征——相对最值点，并依据相对最值点提出一种新的最小二乘直线检测的方法。该方法首先搜索Canny边缘的闭合包络，并寻找闭合包络的相对最值点。包络上相邻相对最值点之间长度满足阈值要求的即为疑似直线的两端点，进而利用最小二乘拟合方法获得该疑似直线的拟合方差，最后利用拟合方差与拟合直线长度之比作为直线判定的标准用来检测出图像中的直线。实验表明，本方法与传统方法如PPHT直线检测、LSD直线检测方法等相比，检测直线的精度更高，检测速度更快。     

某机载探测系统自主回收技术的实现

针对某机载探测系统探头回收自动化程度低的问题，提出了在无需操作员干预情况下实现探头自主回收的技术方法。采用了先进的控制算法，将缆位角与探头、缆长进行结合并引入了绞车控制系统，使绞车对探头的回收速度及缆长可进行实时调整，并给出了3种不同缆位角摆动条件下缆长与探头回收速度的控制曲线关系。研发了自主回收装置，设计了能够实现自主回收功能的探头回收机构。实验验证表明：在无需人为干预的条件下，在缆位控制系统及自主回收机构的配合下，系统实现了对探头快速、安全的回收。     

Manifold structure preservative for hyperspectral target detection

A nonparametric method termed as manifold structure preservative (MSP) is proposed in this paper for hyperspectral target detection. MSP transforms the feature space of data to maximize the separation between target and background signals. Moreover, it minimizes the reconstruction error of targets and preserves the topological structure of data in the projected feature space. MSP does not need to consider any distribution for target and background data. So, it can achieve accurate results in real scenarios due to avoiding unreliable assumptions. The proposed MSP detector is compared to several popular detectors and the experiments on a synthetic data and two real hyperspectral images indicate the superior ability of it in target detection.     

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C₄ index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.     

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。     

移动机器人铣削制孔系统基准检测

结合航空大部件的数字化对接装配需求,设计了一套用于对接面铣削制孔的移动机器人加工系统,并提出具体的加工工艺流程。研究了基于激光轮廓扫描仪的大量散乱点云数据的预处理算法,提出基于栅格法和迭代拟合法的对接面特征提取算法。针对移动机器人加工系统在大场景下的高精度定位问题,提出基于扫描线法和最小二乘法原理的对接基准孔坐标找正算法。通过产品的加工实验验证,对接面铣削和制孔精度满足系统要求的各项技术指标,证明了本文提出的移动机器人铣削制孔系统的装配对接面加工方法能够精确地完成大部件数字化装配任务,对提高装配质量和效率具有重要意义。