采用视觉伺服的月球样品容器夹持控制方法

嫦娥五号探测器月面采样封装任务需利用采样机械臂及其末端执行机构夹持样品容器,为克服非结构化月面环境对机械臂控制造成的不可知影响,确保精确夹持样品容器,提出并设计了一种视觉伺服样品容器夹持的控制方法和系统。系统通过固定安装相机和"眼在手"相机协同获取机械臂末端执行机构以及样品容器特征,采用扩展卡尔曼滤波算法对机械臂末端执行机构位姿的控制参数进行估计,消除控制位姿的轨迹抖动,实现了对样品容器的精确夹持。最后,通过分析在轨月球样品容器被夹持过程的数据和图像,验证了该视觉伺服控制方法和系统设计正确有效。     

面向安全属性的软件组件可信依赖关系度量模型

随着软件的应用规模越来越大，软件系统一旦出现故障，很可能会造成人员伤亡或财产等重大损失，因此对软件可信性进行评估尤为重要.在安全攸关如航空航天等领域，作为可信关键属性之一的安全性影响着整个软件系统的可信评估.特别当一个组件受到安全攻击或缺陷出现时，可信性会下降，会影响到与它可信关联的组件可信性，从而影响到整个系统的可信性.遵循这一思路，设计了软件系统组件可信性发生改变后对其它组件可信性影响度量公式，进一步给出当一组件可信性发生变化，整个软件系统各组件可信性度量发生变化的度量模型.本文以某“航天电源管理设备”为例，证明了本文所建立的组件可信依赖关系度量模型的合理性和有效性.     

自适应航天器态势分析系统

利用深度学习中的卷积神经网络理论,基于单目视觉系统和带有标识物的航天器影像,实现对航天器的三维姿态角、距拍摄点距离和相对拍摄中心偏移量的精准测量。利用机器学习理论实现网络自主学习样本特征,这一方式将大幅降低动态测量的误差。同时,这种测量方式也避免了人工提取特征的复杂过程,实现任意、精准、快速测量,对航天器在组装及发射过程中的姿态估计、距离测算起到关键性作用。     

基于随机维纳过程的航空发动机性能衰退研究

针对发动机下发间隔预测的问题，研究了发动机性能衰退规律。基于发动机排气温度裕度（EGTM）数据，建立了随机维纳模型，并使用严酷度因子修正模型，表征不同的工作条件对发动机的影响。利用期望最大化（EM）算法结合贝叶斯定理，对模型参数求解，最终得到了基于性能衰退的发动机平均下发间隔。研究结果表明，在严酷度因子为1.2696的使用条件下，某型号航空发动机性能衰退平均下发间隔为8219循环；基于该模型预测的发动机平均下发间隔与厂商建议的下发时间之间的误差仅为1.7%。     

小型磁偏转质谱计性能实验研究

自主研发了小型磁偏转质谱计，并对其质量范围、分辨本领、灵敏度和最小可检分压力四个主要性能指标进行了实验测试和比对。结果表明，该质谱计的质量范围为（1~143）amu，相对N₂的分辨本领为1400，灵敏度为4.2×10^-5 A/Pa，最小可检分压力为9.5×10^-7 Pa。该测试和比对结果为磁偏转质谱计进一步的优化设计和性能提高奠定了重要基础。     

Numerical analysis of hypersonic thermochemical non-equilibrium environment for an entry configuration in ionized flow

A theoretical methodology for thermochemical non-equilibrium flow combing with the HLLC (Harten-Lax-van Leer Contact) scheme was applied to study the hypersonic thermochemical non-equilibrium environment of an entry configuration in ionized flow. A two-temperature controlling model was utilized and the Gupta’s 11 species (N₂, O₂, NO, O, N, NO⁺, N₂⁺, O₂⁺, N⁺, O⁺, e^?) thermochemical non-equilibrium model was taken. Firstly, numerical calculations of hypersonic thermochemical non-equilibrium environments for different aerodynamic shapes were carried out to verify the reliability of the method above. Then, the method was used to research the effects of ionization and wall catalysis on the hypersonic thermochemical non-equilibrium environment of the entry configuration in ionized flow. The shock stand-off distance can be reduced by thermochemical reactions but doesn’t continue to decrease significantly when ionization occurs. The shock stand-off distance calculated by the 11 species model is 4.2% smaller than that calculated by the 5 species (N₂, O₂, NO, O, N) thermochemical non-equilibrium model without considering ionization. Ionization reduces wall heat flux but increases wall pressure a little. The effect of ionization on aerothermal loads is greater than that of aerodynamic loads. The thermochemical reactions of electrons and ions catalyzed at the wall increase wall heat flux significantly but make a small change in wall pressure. The maximum wall heat flux obtained by only considering the electrons and ions catalyzed at the partially catalytic wall condition is 11.8% less than that calculated at the super-catalytic wall condition.     

Variational Mode Decomposition for Rotating Machinery Condition Monitoring Using Vibration Signals

The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for ensuring both the efficiency and accuracy of the monitoring process.Variational mode decomposition(VMD)is a signal processing method which decomposes a non-stationary signal into sets of variational mode functions(VMFs)adaptively and non-recursively.The VMD method offers improved performance for the condition monitoring of rotating machinery applications.However,determining an accurate number of modes for the VMD method is still considered an open research problem.Therefore,a selection method for determining the number of modes for VMD is proposed by taking advantage of the similarities in concept between the original signal and VMF.Simulated signal and online gearbox vibration signals have been used to validate the performance of the proposed method.The statistical parameters of the signals are extracted from the original signals,VMFs and intrinsic mode functions(IMFs)and have been fed into machine learning algorithms to validate the performance of the VMD method.The results show that the features extracted from VMD are both superior and accurate for the monitoring of rotating machinery.Hence the proposed method offers a new approach for the condition monitoring of rotating machinery applications.     

基于混合有限元解析法的永磁同步电机永磁体电涡流损耗估计

永磁同步电机永磁体受限于热约束,无法在温度较高的环境下运行,故需减少永磁体上的电涡流损耗,从而降低永磁体上的温度。针对使用有限元法对永磁体电涡流损耗估算时间较长,以及使用解析法估算时难以达到与有限元法相同的精度,采用混合有限元解析法估算永磁体上的电涡流损耗。结合电涡流的反作用,在模拟电机旋转时,无需重复划分三角形区域；使用MATLAB软件仿真模拟,将混合有限元解析法与Galerkin有限元法对比,减少三角形区域划分的个数。由此验证了永磁体上电涡流损耗符合端部效应以及集肤效应的特征,在保证精度的同时,减少了仿真的时间。