磨粒监测与分析系统在航空发动机状态监控中的应用(英文)

磨损颗粒是研究机器磨损状态时最直接、最重要的信息元 ,通过对滑油中的磨粒进行监测与分析以判断机械设备的磨损状况 ,可以监测并预防机械设备的磨损故障。本文介绍的磨粒监测与分析系统 ( DMAS)具有制谱简单、自动化程度高、能及时准确预报及诊断机器的各种磨损类故障等特点 ,可以有效地保障机器安全可靠地运行。本文详细地阐述了这套系统的基本原理、软硬件组成 ,以及在航空发动机磨损状态监测中的应用     

原子谱线参数的辨识方法

在一种原子谱线参数测量的辨识方法基础上编制了一套原子港线参数辨识的标准软件。用此软件对纯氩和氩、氮混合非平衡电弧等离子体射流的原子谱线进行了处理，获得了射流的电子温度、气体平动温度、电子和原子数密度。状态诊断表明；实验条件下的等离子体射流除中心点为ＬＴＥ外非平衡效应是非常显著的。     

星图匹配制导中的关键技术

为了提高新一代弹道导弹的快速、机动反击能力和射击精度，采用星光，惯性组合制导足最佳的选择。本论文给出了基于双星敏感器的、星图匹配制导系统的关键技术。提出了根据弹道设计导航星表．极大地简化了弹载星表；提出了一种适用于星光制导的凸多边形箅法，合理地减少了匹配的星对角距数目。这两项技术保证了星图匹配制导技术的实用性。提出了一种分离初始定位、定向误差及平台漂移误差方法。采用凸多边形的星图识别算法可同时获得多颗星的瞬时位置，结合恒星在像平面的位置可解算导弹在赤道惯性系和发射点惯性系的三轴姿态，最后，给出了导弹初始定位、定向误差的数学表达式。仿真结果表明了该方法的有效性。     

卫星太阳能帆板的撞击问题

本文研究卫星帆板展开过程中的撞击问题，用子结构方法和单向递推组集方法行到经铰坐标和柔性体模态坐标为系统广义坐标的撞击阶段柔性多体系统的动力学方程，由与撞击有关约束方程的Ｌａｇｒａｎｇｅ乘子导出撞击力的计算公式，计算了卫星帆板在撞击过程中撞击力的最大值和卫星姿态角速度的变化规律。计算结果表明，对柔性体之间的碰撞，卫星姿态角速度在变化过程中呈现上下波动的趋势。     

基于Volterra级数的非线性非定常气动力降阶模型

发展了一种基于Volterra级数的非线性非定常气动力降阶模型。着重探讨了基于CFD／CSD耦合计算的Volterra核辨识方法。CFD／CSD耦合计算采用了一种简单的动网格快速生成方法，气动网格与结构网格节点间的物理信息交换采用一种改进的CVT方法。通过与CFD／CSD直接计算的结果对比，表明非定常气动力Volterra级数模型是一种效率高、精度好、能描述非线性、便于使用的降阶模型，特别适用于预测结构做非定常运动下的非定常气动力。     

推进剂稳态燃速最优化辨识

考虑侵蚀燃烧对推进剂稳态燃速的影响,将推进剂稳态燃速模型参数辨识问题转化为非线性规划问题求解。采用Powell方法和并行遗传算法组成的混合优化方法,提高了非线性规划问题求解的效率和质量,得到了推进剂稳态燃速模型参数的全局最优辨识值。通过对设计空间的可视化进行优化收敛过程的参数分析,近似得到侵蚀燃烧模型参数的不确定性区间。     

挠性空间结构变形的自适应辨识

本文以空间控制中的挠性结构变形的辨识为背景,以李雅普诺夫稳定性理论为基础,从辨识的一致性原则出发,给出时变参数的自适应辨识算法。该算法具有梯度型递推形式,结构简单、容易实现。计算机模拟结果表明该算法是解决时变参数辨识的一种有效方法。     

Motion prediction of a non-cooperative space target

Capturing a non-cooperative space target is a tremendously challenging research topic. Effective acquisition of motion information of the space target is the premise to realize target capture. In this paper, motion prediction of a free-floating non-cooperative target in space is studied and a motion prediction algorithm is proposed. In order to predict the motion of the free-floating non-cooperative target, dynamic parameters of the target must be firstly identified (estimated), such as inertia, angular momentum and kinetic energy and so on; then the predicted motion of the target can be acquired by substituting these identified parameters into the Euler’s equations of the target. Accurate prediction needs precise identification. This paper presents an effective method to identify these dynamic parameters of a free-floating non-cooperative target. This method is based on two steps, (1) the rough estimation of the parameters is computed using the motion observation data to the target, and (2) the best estimation of the parameters is found by an optimization method. In the optimization problem, the objective function is based on the difference between the observed and the predicted motion, and the interior-point method (IPM) is chosen as the optimization algorithm, which starts at the rough estimate obtained in the first step and finds a global minimum to the objective function with the guidance of objective function’s gradient. So the speed of IPM searching for the global minimum is fast, and an accurate identification can be obtained in time. The numerical results show that the proposed motion prediction algorithm is able to predict the motion of the target.     

FUZZY IDENTIFICATION METHOD BASED ON A NEW OBJECTIVE FUNCTION

Many fuzzy models on modeling and controlmethods have been developed since Takagi-Sugeno model was proposed[1] .The main prob-lems of the fuzzy identifying method based on T-S model appear in the following two aspects:　　 1 It is important what heuristic knowledgeis chosen to acquire the premise structure of theT- S model.This problem is related to the opti-mal partition of input space of premise part andmodeling accuracy.　　 2 Itis crucial whatidentifying method is u-tilized to estimate …     

Real-time fault detection method based on belief rule base for aircraft navigation system

Real-time and accurate fault detection is essential to enhance the aircraft navigation system’s reliability and safety. The existent detection methods based on analytical model draws back at simultaneously detecting gradual and sudden faults. On account of this reason, we propose an online detection solution based on non-analytical model. In this article, the navigation system fault detection model is established based on belief rule base (BRB), where the system measuring residual and its changing rate are used as the inputs of BRB model and the fault detection function as the output. To overcome the drawbacks of current parameter optimization algorithms for BRB and achieve online update, a parameter recursive estimation algorithm is presented for online BRB detection model based on expectation maximization (EM) algorithm. Furthermore, the proposed method is verified by navigation experiment. Experimental results show that the proposed method is able to effectively realize online parameter evaluation in navigation system fault detection model. The output of the detection model can track the fault state very well, and the faults can be diagnosed in real time and accurately. In addition, the detection ability, especially in the probability of false detection, is superior to offline optimization method, and thus the system reliability has great improvement.