应用于卫星自主任务调度的改进遗传算法

针对具有侧摆能力的对地观测卫星的自主任务调度问题,对卫星自主任务调度问题和约束条件进行了描述,针对卫星自主任务调度NP-hard的特点,构建了基于目标收益及多约束卫星任务调度模型。设计了一种改进的遗传算法,从遗传操作的各个部分进行算法优化。首先将小区间法应用于初始种群生成,保证了种群的多样性,并且交叉和变异算子均引入自适应概率;同时采用两代竞争技术来避免“早熟”现象,提高算法的效率和鲁棒性。算法还采用最优保留策略用来保存进化中的最优解,使得算法收敛于全局最优。对局部多冲突观测任务应用该改进遗传算法,并针对区域密集目标的观测问题设计了仿真试验,与传统模拟退火算法及免疫蚁群遗传混合算法进行了比较,验证了该算法的有效性和收敛效果。     

小失效概率情况下的全局可靠性灵敏度分析的高效方法

针对目前很多算法都无法准确、高效地计算小失效概率（10^-4,甚至更小）情况下的全局可靠性灵敏度问题,本文提出了一种高效求解小失效概率情况下的全局可靠性灵敏度新算法。所提算法通过扩大标准差构造重要抽样密度函数来进行空间分割（SP）,再与无迹变换（UT）结合,利用函数在分割后的子空间内非线性程度的降低和无迹变换方法可以高效计算低非线性程度函数的前二阶矩,来高效准确地计算小失效概率情况下的全局可靠性灵敏度。所提算法的优点有：重要抽样密度函数的选择可以使得空间分割时向重要区域偏移,并且在分割区域内功能函数的复杂性被降低,从而可以利用无迹变换方法高效计算失效概率,进而高效求得全局可靠性灵敏度。与已有的算法相比,算例说明了本文所提方法的优势。     

Application of Analytic Redundancy-based Fault Diagnosis of Sensors to Onboard Maintenance System

Analytic redundancy-based fault diagnosis technique (ARFDT) is applied to onboard maintenance system (OMS). The principle of the proposed ARFDT scheme is to design a redundancy configuration using ARFDT to enhance the functions of redundancy management and built in test equipment (BITE) monitor. Redundancy configuration for dual-redundancy and analytic redundancy is proposed, in which, the fault diagnosis includes detection and isolation. In order to keep the balance between rapid diagnosis and binary hypothesis, a filter together with an elapsed time limit is designed for sequential probability ratio test (SPRT) in the process of isolation. Diagnosis results would be submitted to central maintenance computer (CMC) together with BITE information. Moreover, by adopting reconstruction, the designed method not only provides analytic redundancy to help redundancy management, but also compensates the output when both of the sensors of the same type are faulty. Our scheme is applied to an aircraft’s sensors in a simulation experiment, and the results show that the proposed filter SPRT (FSPRT) saves at least 50% of isolation time than Wald SPRT (WSPRT). Also, effectiveness, practicability and rapidity of the proposed scheme can be successfully achieved in OMS.     

随机事元及其在解决矛盾问题中的应用

通过建立随机事件的可拓模型,给出了随机事元、随机事元的概率、随机事元集和可拓事件集的概念。在对随机事元可拓性研究的基础上给出了随机事元的拓展概率的概念。利用随机事元的多特征性,讨论了当随机事元的某一个或若干个特征及其量值改变时,由可拓变换的传导性,将在随机事元的各要素之间产生传导效应,从而导致随机事元概率的改变,进而为涉及随机事件发生的概率的矛盾问题的解决提供了有效途径。最后举例说明了利用概率的改变处理矛盾问题的方法。     

测向交叉定位系统中的最优交会角研究

研究了测向交叉定位系统中的最优交会角问题。在目标到两部被动传感器基线之间的垂直距离不同的情况下和最小圆概率误差（CEP）准则下,得出了一些有意义的结论。研究表明,最优交会角跟目标到两部传感器基线的垂直距离与基线长度的比值l有关。当l≥√3/6时,只存在一个最优交会角且此时目标与两部被动传感器呈等腰三角形,并在此情况下分析了l的变化对圆概率误差的影响。当l＜√3/6时,存在两个相等的最优交会角。得出的结论在多个被动传感器的优化配置进而提高目标定位精度方面具有一定的理论和实际意义。     

概率空间中单调事件序列的连续性

研究了概率空间中事件序列的可列可加性和有限可加性,并提出事件序列次可加性的概念,证明了单调事件序列的连续性,并给出一个充分必要条件.     

信息战软杀伤效应模型研究

软杀伤是信息战的重要杀伤手段,能使硬武器威力倍增。就信息战软杀伤效应模型进行了研究。分析了软杀伤的量化基础,建立了信息战软杀伤效应的定量分析模型和群体软毁伤传播模型。     

滚动轴承摩擦力矩的乏信息模糊预报

滚动轴承摩擦力矩的波动具有不确定性,属于概率分布与趋势规律都未知的乏信息系统.这阻碍了对轴承摩擦力矩的小样本分析与总体把握.为此,以模糊集合理论为基础,从小样本入手,建立了航天轴承摩擦力矩参数的经验概率密度函数及其模糊预报模型,并对摩擦力矩的均值上界和最大值上界进行预报.对HKTA和HKTB两种轴承摩擦力矩参数的试验研究表明,在99%～100%置信水平下,预报结果与试验结果之间的误差很小,最大绝对误差仅为0.081 1个设定单位,最大相对误差仅为10.165%,可以满足航天工程的要求.      

Statistical properties of fatigue damage of Gaussian random loadings

Firstly, the fatigue damages associated with the random loadings were always deemed as high-cycle or very-high-cycle fatigue problems, and based on Chebyshev theorem, the number of rainflow cycles in a given time interval could be recognized as a constant by neglecting its randomness. Secondly, the randomness of fatigue damage induced by the distribution of rainflow cycles was analyzed. According to central limit theorem, the fatigue damage could be assumed to follow Gaussian distribution, and the statistical parameters: mean and variance, were derived from Dirlik's solution. Finally, the proposed method was used to a simulate Gaussian random loading and the measured random loading from an aircraft. Comparisons with observed results were carried out extensively. In the first example, the relative errors of the proposed method are 2.29%, 3.52% and 1.16% for the mean, standard deviation and variation coefficient of fatigue damage, respectively. In the second example, these relative errors are 11.70%, 173.32% and 18.20%, and the larger errors are attributable to non-stationary state of the measured loading to some extent.