时变最优条件数的目标要素解算方法

纯方位目标跟踪是一个典型的非线性问题,伪线性跟踪估计器是理论和工程上解决该问题的一类重要方法。针对纯方位观测模型伪线性化后,系统存在弱可观测性问题,提出了一种时变最优条件数的目标要素解算新方法,理论上给出了一个改善系统可观测性的最优乘数因子。在系统条件数和参数估计的收敛概率两个方面,数值仿真和实验数据验证都表明新方法要优于经典纯方位伪线性化方法。     

坦克火力系统通用技术状态指标体系构建

我军对某型坦克火力系统实施状态维修,准确的技术状态评估是状态维修的前提,技术状态指标体系是进行火力系统状态评估的依据。构建指标体系的过程中,首先应用层次分析法构建技术状态参数体系的层次结构,然后采用云模型的方法获取参数体系中各定性、定量参数的相对重要度等级,依此使参数体系最优化。实例证明,上述方法有效解决了构建坦克火力系统技术状态指标体系的构建中分类不明确、筛选困难等问题。     

摩擦条件对纳米铜润滑添加剂减摩性能影响的研究

研究了摩擦速度、载荷和摩擦表面处理方式等摩擦条件,对纳米铜润滑添加剂减摩性能的影响,发现摩擦速度较高时,载荷增加使摩擦因数明显降低;在低载荷时摩擦速度的减小,使摩擦因数降低了17%,载荷增大后摩擦速度的变化,对摩擦因数的影响减小.摩擦表面抛光处理,使摩擦因数降低为磨削处理时的74%.     

Numerical simulation of warhead transportation

The new numerical approach for analysis of the warhead transportations is suggested.This approach allows to control the warhead operability before its experimental analysis.The approach is implemented by the adequate models for the software ANSYS.Analysis of the loads at land operations and trans-portations of the warhead by natural roads,water and aviation allows to obtain the maximal values of loads,which are used in numerical simulations of the warhead.These loads give an opportunity to analyze the operability and the fatigue strength of the cartridge warhead.The numerical simulations of the attachments of the warhead combat elements are performed on the basis of the suggested method.The data of the numerical simulations verifies the operability of the fastener system of the warhead combat elements.     

An efficient SPH methodology for modelling mechanical characteristics of particulate composites

Particulate composites are one of the widely used materials in producing numerous state-of-the-art components in biomedical, automobile, aerospace including defence technology. Variety of modelling techniques have been adopted in the past to model mechanical behaviour of particulate composites. Due to their favourable properties, particle-based methods provide a convenient platform to model failure or fracture of these composites. Smooth particle hydrodynamics (SPH) is one of such methods which demonstrate excellent potential for modelling failure or fracture of particulate composites in a Lagrangian setting. One of the major challenges in using SPH method for modelling composite materials depends on accurate and efficient way to treat interface and boundary conditions. In this paper, a master-slave method based multi-freedom constraints is proposed to impose essential boundary conditions and interfacial displacement constraints in modelling mechanical behaviour of composite materials using SPH method. The proposed methodology enforces the above constraints more accurately and requires only smaller condition number for system stiffness matrix than the procedures based on typical penalty function approach. A minimum cut-off value-based error criteria is employed to improve the compu-tational efficiency of the proposed methodology. In addition, the proposed method is further enhanced by adopting a modified numerical interpolation scheme along the boundary to increase the accuracy and computational efficiency. The numerical examples demonstrate that the proposed master-slave approach yields better accuracy in enforcing displacement constraints and requires approximately the same computational time as that of penalty method.     

基于油液分析的自行火炮装备性能监测研究

提出了一种基于油液光谱分析技术的自行火炮装备磨损故障诊断方法 ,该方法主要包括用于监测润滑油中磨损金属颗粒浓度变化的模型 ,并制定了各主要磨损金属元素的浓度和梯度界限值 ,并将其分为三个层次。同时将灰色建模理论引入光谱分析领域 ,通过拟合光谱仪分析的数据 ,掌握润滑油中磨损金属颗粒浓度的变化规律 ,进而对其浓度变化规律进行预测 ,从而达到对装备进行性能监测的目的。     

多载荷识别频响函数矩阵求逆法的改进算法

分析了频响函数矩阵求逆法在多载荷识别中存在的问题 ,提出了两种改进算法 ,即算术平均法和条件数加权平均法。实验结果表明 ,这两种方法在识别精度和消除病态影响方面均优于传统的频域识别方法。     

涡轮泵状态监控及传感器故障识别的新异类检测方法

为了在故障样本稀缺、故障模式不完备的情况下监控涡轮泵状态,并剔除传感器失效故障造成的虚警,提出用于涡轮泵状态监控及传感器故障识别的单类支持向量机新异类检测方法。该方法以正常状态为目标类构建单类支持向量机检测器,用于检测涡轮泵是否出现异常;以传感器故障为目标类构建单类支持向量机检测器,用于判断检测到的异常是否属于传感器故障。对涡轮泵试车数据的分析结果表明了该方法的有效性。     

基于风险评估的检测与修理决策模型

研究了基于故障风险的检测与维修策略,运用威布尔比例风险模型建立系统故障率与工作时间及所处状态的关系。分析了两类基于间接状态信息维修决策的风险,建立了系统检测及维修策略的决策树,通过比较检测与不检测情况下的期望费用确定最优的检测间隔。算例表明,所提出的方法能够有效控制系统运行风险,降低系统运行费用。     

控制不等式的改进

讨论了随机过程控制关系产生的不等式,对这个不等式给出了一个新系数,改进了以前的结果,并将其应用于局部平方可积鞅.