一种应用于实时深空干涉测量的电离层时延修正方法

针对"嫦娥4号"中继星任务S频段信标信号的高精度实时干涉测量需求,结合深空测控干涉测量系统采用的稀疏标校工作模式,研究验证了一种面向测控模式实时干涉测量的电离层时延修正方法。首先分析了电磁波经电离层传播的延迟机理及特性;基于深空站历史观测数据,通过自相关函数分析验证了天顶向TEC的周日特性;在此基础上,结合深空干涉测量中心数据处理设备软件系统,讨论了电离层时延修正方法;通过任务期间的实测数据处理分析,验证了所提方法可以将实时测量精度提升1～3 ns,对低仰角跟踪弧段,该技术方法优势更为明显。该方法为后续深入推进深空测控干涉测量系统在任务中的实时应用提供了技术储备。     

Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

Rotating machinery is widely applied in industrial applications. Fault diagnosis of rotating machinery is vital in manufacturing system, which can prevent catastrophic failure and reduce financial losses. Recently, Deep Learning (DL)-based fault diagnosis method becomes a hot topic. Convolutional Neural Network (CNN) is an effective DL method to extract the features of raw data automatically. This paper develops a fault diagnosis method using CNN for InfRared Thermal (IRT) image. First, IRT technique is utilized to capture the IRT images of rotating machinery. Second, the CNN is applied to extract fault features from the IRT images. In the end, the obtained features are fed into the Softmax Regression (SR) classifier for fault pattern identification. The effectiveness of the proposed method is validated using two different experimental data. Results show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method.     

Predictive fault-tolerant control of an all-thruster satellite in 6-DOF motion via neural network model updating

The problem of controlling an all-thruster spacecraft in the coupled translational-rotational motion in presence of actuators fault and/or failure is investigated in this paper. The nonlinear model predictive control approach is used because of its ability to predict the future behavior of the system. The fault/failure of the thrusters changes the mapping between the commanded forces to the thrusters and actual force/torque generated by the thruster system. Thus, the basic six degree-of-freedom kinetic equations are separated from this mapping and a set of neural networks are trained off-line to learn the kinetic equations. Then, two neural networks are attached to these trained networks in order to learn the thruster commands to force/torque mappings on-line. Different off-nominal conditions are modeled so that neural networks can detect any failure and fault, including scale factor and misalignment of thrusters. A simple model of the spacecraft relative motion is used in MPC to decrease the computational burden. However, a precise model by the means of orbit propagation including different types of perturbation is utilized to evaluate the usefulness of the proposed approach in actual conditions. The numerical simulation shows that this method can successfully control the all-thruster spacecraft with ON-OFF thrusters in different combinations of thruster fault and/or failure.     

Bayesian network-based extraction of lunar impact craters from optical images and DEM data

Impact craters are among the most noticeable geomorphological features on the planetary surface and yield significant information about terrain evolution and the history of the solar system. Thus, the recognition of impact craters is an important branch of modern planetary studies. Aiming at addressing problems associated with the insufficient and inaccurate detection of lunar impact craters, a decision fusion method within the Bayesian network (BN) framework is developed in this paper to handle multi-source information from both optical images and associated digital elevation model (DEM) data. First, we implement the edge-based method for efficiently searching crater candidates which are the image patches that can potentially contain impact craters. Secondly, the multi-source representations of an impact crater derived from both optical images and DEM data are proposed and constructed to quantitatively describe the two-dimensional (2D) and three-dimensional (3D) morphology, consisting of Histogram of Oriented Gradient (HOG), Histogram of Multi-scale Slope (HMS) and Histogram of Multi-scale Aspect (HMA). Finally, a BN-based framework integrates the multi-source representations of impact craters, which can provide reductant and complementary information, for distinguishing craters from non-craters. To evaluate the effectiveness and robustness of the proposed method, experiments were conducted on three lunar scenes using both orthoimages from the Lunar Reconnaissance Orbiter (LRO) and DEM data acquired by the Lunar Orbiter Laser Altimeter (LOLA). Experimental results demonstrate that integrating optical images with DEM data significantly decreases the number of false positives compared with using optical images alone, with F₁-score of 84.8% on average. Moreover, compared with other existing fusion methods, our proposed method was quite advantageous especially for the detection of small-scale craters with diameters less than 1000 m.     

月球表层采样样品智能确认方法

表层采样是月球采样探测的重要方式，样品智能确认有助于提升工作效率与复杂问题处理能力。结合月球表层采样铲挖工作过程，分析了铲挖过程中臂载相机图像的特点，模仿有人参与识别过程，提出了层次解耦的月球样品智能识别流程，利用深度学习方法构建了一类深度卷积识别网络，完整地描述了图像、特征、标记在网络中的正反传递关系，并在月球表层采样地面试验中进行了验证，结果表明该方法对不同光照、不同背景、不同过程、不同形态的样品，具有较好的泛化识别能力，误识别率优于8.1%，平均单幅识别时间约0.7 s。     

基于改进的卷积神经网络的步态识别

为了提高惯性传感器采集到的序列数据中步态识别的准确率,建立了一个激励层改进的卷积神经网络(CNN)模型。针对三轴加速度传感器对运动太过敏感导致步态周期划分不准确的问题,采用加速度传感器与弯曲度传感器组合获取人体运动信息。将CNN模型中激励层的线性整流函数(ReLU)改进为带泄露线性整流函数(Leaky ReLU),以解决遇到卷积输出数据小于0时神经元被抑制的问题,进而达到提高步态识别准确率的目的。实验结果表明激励层优化的CNN模型在行走、上下楼和上下坡五种步态模式下识别率达到了95.79%,与未采用弯曲度传感器的改进CNN模型和未进行激励层改进的CNN模型相比,步态识别率有所提高。     

神经网络模型在压气机通流特性分析中的应用

为了解决通流特性分析程序中原始模型对压气机性能预测精度不足的问题,提高压气机通流特性分析过程的可靠性,基于对大量多圆弧叶栅的数值模拟结果建立了压气机叶栅性能数据库,并以该数据库为依托,采用神经网络建模方法建立了压气机叶栅基准损失系数和基准落后角模型。结果显示:两模型对叶栅基准损失系数和基准落后角的预测精度均满足工程应用要求,其精度分别为±0.002和±1°。在对采用神经网络模型的通流特性分析程序校验过程中发现,其无论对压气机整机性能还是对流动细节的预测精度上都获得了显著提高,尤其是在主流区。此外从压气机整体特性上看,基准损失系数和基准落后角精度的提高对非设计工况损失系数和落后角的预测精度影响是积极的。      

基于自联想网络的发动机传感器解析余度技术

本文提出了一种基于自联想神经网络的传感器解析余度技术。在这种网络中,冗余传感器的信息被压缩、重组进入网络的第一部分,网络的第二部分将压缩信息恢复出来。基于数据融合原理,若一个传感器发生故障,其它传感器仍可提供足够的信息代替发生故障的传感器。理论分析和用于涡轴发动机的仿真结果表明,这种特殊结构的自联想网络具有良好的过滤噪声和故障信号的作用,特别适合于用作不易建模的复杂对象的传感器信号重构     

基于故障树和神经网络模型的航天器智能诊断研究

提出基于故障树和神经网络模型的诊断方法,提出面向故障树的基于框架和广义规则的知识表示方法及相应的确定性和可能性推理策略,对于可能性推理的结果,通过基于神经网络模型的学习诊断来进一步确定其状态。在Ｗｉｎｄｏｗｓ环境下,用Ｂｏｒｌａｎｄ Ｃ＋＋实现了一个原型系统。通过对“实践４号”卫星能源系统故障模拟实验台的诊断验证了系统的有效性。     

基于FDDI网络的空间站DMS网络可靠性分析

在首先介绍了空间站ＤＭＳ网络结构的基础上,通过对ＤＭＳ网络使用的ＦＤＤＩ、ＭＩＬ－ＳＴＤ－１５３３Ｂ及ＩＥＥＥ８０２．４网络在各种情况下的可靠性分析,应用概率分析的方法得出了系统可靠性的数学模型,通过图示得出ＤＭＳ网络可靠性随站点数及链路失效概率的变化情况,为空间站ＤＭＳ网络可靠性设计提供了依据。