摘要
为提升底架吊装设备的可靠性分析精度,提出基于GA-UGF模型的结构可靠性分析方法。首先,基于结构静态响应分析结果,建立结构参数化分析模型并开展试验设计;其次,依据试验设计结果,统计结构各随机变量观测值及出现概率,计算各随机变量质量矩并为各质量矩添加修正系数,以结构响应的试验及理论质量矩误差最小化为目标函数,利用遗传算法提升传统UGF模型的表述精度,进而结合可靠性理论,构建结构可靠性分析模型;最后,以某型号底架吊装设备为研究对象,利用所提方法对其进行结构可靠性分析,并将分析结果与传统方法进行对比。对比结果表明:所构建的GA-UGF模型能有效提升结构信息传递合理性;基于GA-UGF模型获取的可靠性指标更加接近蒙特卡洛法。该结论为进一步提升结构可靠性能提供一定参考。
In order to improve the reliability analysis accuracy of underframe hoisting equipment,a structural reliability analysis method based on GA-UGF model was proposed.Firstly,based on the results of structural static response analysis,a structural parametric analysis model was established and experimental design was carried out.Secondly,according to the experimental design results,the observed values and occurrence probabilities of each random variable in the structure were counted,the mass moments of each random variable were calculated and the correction coefficients were added to each mass moment,and the experimental and theoretical mass moment errors of the structure response were minimized as the objective functions,and the genetic algorithm was used to improve the expression accuracy of the traditional UGF model,and then the structural reliability analysis model was constructed in combination with the reliability theory.Finally,taking a certain type of underframe hoisting equipment as the research object,the proposed method was used to analyze the structural reliability,and the analysis results were compared with the traditional methods.The comparison results show that the constructed GA-UGF model can effectively improve the rationality of structural information transmission.The reliability index obtained based on the GA-UGF model is closer to the Monte Carlo method.This conclusion can provide a certain reference for further improving the reliability performance of the structure.
作者
曹阳
耿军
张越
姚成勇
王慎轩
CAO Yang;GENG Jun;ZHANG Yue;YAO Chengyong;WANG Shenxuan(CRRC Nanjing Puzhen Co.,Ltd.,Nanjing,Jiangsu 210031,China;Nanjing CRRC Puzhen Urban Rail Vehicle Co.,Ltd.,Nanjing,Jiangsu 210031,China;Harbin Railway Technical College,Harbin,Heilongjiang 150000,China)
出处
《机车车辆工艺》
2024年第2期1-5,9,共6页
Locomotive & Rolling Stock Technology
关键词
吊装设备
可靠性分析
遗传算法
通用生成函数
地铁车辆
hoisting equipment
reliability analysis
genetic algorithm
universal generating function
urban rail vehicle
