多系统协同仿真策略的火炮协调器可靠性分析  

Reliability Analysis of Self-Propelled Artillery Coordinator Based on Cooperative Simulation Strategy

作　　者：王鹏 孙志礼[1] 骆海涛[2] 刘勤[3] WANG Peng;SUN Zhi-li;LUO Hai-tao;LIU Qin(Institute of Mechanical Engineering and Automation,Northeastern University,Liaoning Shenyang 110819,China;Shenyang Institute of Automation,Chinese Academy of Sciences,Liaoning Shenyang 110016 China;Ordnance Science and Research Academy of China,Beijing 100089,China)

机构地区：[1]东北大学机械工程与自动化学院,辽宁沈阳110819 [2]中国科学院沈阳自动化研究所,辽宁沈阳110016 [3]中国兵器科学研究院,北京100089

出　　处：《机械设计与制造》2022年第3期87-90,96,共5页Machinery Design & Manufacture

基　　金：国家自然科学基金资助项目(51775097);国防技术基础项目(JSZL2015208B001)。

摘　　要：为了准确计算某自行火炮协调器可靠性,基于协同仿真策略考虑机械、液压和控制系统的参数随机性建立协调器系统参数化模型。采用基于Kriging和Monte Carlo的方法对协调器进行位姿可靠度计算。为了快速提高Kriging模型的准确性,选择使学习函数值最小的样本点代入模型中。提出了一种学习停止条件,保证了样本点符号预测精度且学习次数明显减少。计算结果表明:协调器位姿可靠度为99.82%,所提方法和AK-MCS+U(Active learning and Kriging-based Monte-Carlo Simulation+U function)相比失效概率相差0.001%,功能函数调用次数减少了38.27%,计算时间减少了37.6%。方法较好的解决了工程上隐式且非线性程度较高,仿真时间过长的问题。In order to accurately calculate the reliability of a self-propelled artillery coordinator,a parametric model of the coordinator system is established based on the co-simulation strategy considering the randomness of the parameters of the mechanical,hydraulic and control systems.The method based on Kriging and Monte Carlo was used to calculate the pose reliability of the coordinator.In order to improve the accuracy of Kriging model quickly,the sample points which minimize the value of learning function were selected to be substituted into the model.A learning stop condition was proposed,which can not only ensure the prediction accuracy of the sample point symbols and reduce the learning times significantly.The calculation result shows that the pose reliability of the coordinator is 99.82%.Compared with AK-MCS+U(Active learning and Kriging-based Monte-Carlo Simulation+U function),the difference of failure probability is 0.001%,the number of function calls is reduced by 38.27%,and the calculation time was reduced by 37.6%.The method proposed in this paper solves the problems of implicit,high non-linearity and long simulation time better in engineering.

关 键 词：可靠性 KRIGING Monte Carlo 学习函数 失效概率 

分 类 号：TH16[机械工程—机械制造及自动化] TB114[理学—概率论与数理统计]