基于径向基神经网络的液压支架前连杆可靠性评估研究
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摘要
为了解决液压支架前连杆可靠性评估困难的问题,提出了一种基于径向基神经网络的液压支架前连杆可靠性评估的方法。首先使用ANSYS参数化语言APDL对液压支架前连杆进行静力分析,确定与前连杆可靠性相关的设计变量;再使用ANSYS/PDS模块结合拉丁超立方抽样法对前连杆进行可靠性分析,获取多组前连杆不同设计变量的可靠度;最后使用径向基(RBF)神经网络拟合设计变量与可靠性之间的函数关系,建立前连杆可靠性评估模型,预测前连杆的可靠度。计算结果表明,前连杆可靠度计算结果的最大相对误差为4.46%,最小误差为1.59%。证明了径向基神经网络应用于液压支架前连杆可靠性评估的可行性,为液压支架前连杆可靠性评估提供了新的方法与思路。
In order to solve the problem that the reliability evaluation of the front connecting rod of the hydraulic support was difficult,a method based on radial basis function(RBF)neural network was proposed to evaluate the reliability of the front connecting rod of the hydraulic support.Firstly,by using the ANSYS parametric language APDL,the static analysis of the front connecting rod of the hydraulic support was carried out to determine the design variables related to the reliability of the front connecting rod.Then,the reliability analysis on the front connecting rod was carried out by using the ANSYS/PDS module and the Latin hypercube sampling method,so as to obtain the reliability of the front connecting rods with different design variables.Finally,the RBF neural network was used for fitting the functional relationship between design variables and reliability,establishing the reliability evaluation model of the front connecting rod,and predicting its reliability.The results showed that the maximum relative error of the calculation of the front connecting rod reliability was 4.46%and the minimum error was1.59%.It was proved that the RBF neural network was applied to the reliability evaluation of the front connecting rod of the hydraulic support,and provided a new method and idea for the reliability evaluation of the front connecting rod of the hydraulic support.
In order to solve the problem that the reliability evaluation of the front connecting rod of the hydraulic support was difficult,a method based on radial basis function(RBF)neural network was proposed to evaluate the reliability of the front connecting rod of the hydraulic support.Firstly,by using the ANSYS parametric language APDL,the static analysis of the front connecting rod of the hydraulic support was carried out to determine the design variables related to the reliability of the front connecting rod.Then,the reliability analysis on the front connecting rod was carried out by using the ANSYS/PDS module and the Latin hypercube sampling method,so as to obtain the reliability of the front connecting rods with different design variables.Finally,the RBF neural network was used for fitting the functional relationship between design variables and reliability,establishing the reliability evaluation model of the front connecting rod,and predicting its reliability.The results showed that the maximum relative error of the calculation of the front connecting rod reliability was 4.46%and the minimum error was1.59%.It was proved that the RBF neural network was applied to the reliability evaluation of the front connecting rod of the hydraulic support,and provided a new method and idea for the reliability evaluation of the front connecting rod of the hydraulic support.
引文
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[2]Avontuur G C,Werff K V D.Systems reliability analysis of mechanical and hydraulic drive systems[J].Reliability Engineering&System Safety,2002,77(2):121-130.
[3]武红霞,秦冬晨.基于有限元的液压支架顶梁可靠性分析[J].煤矿机械,2010,31(10):98-99.
[4]何富连,殷帅峰,李通达,等.基于共因失效计算模型的支架液压系统可靠性分析[J].煤矿安全,2013,44(11):64-67.
[5]丁飞,王谦.液压支架结构疲劳动态可靠性评估方法[J].中国安全科学学报,2015,25(6):86-90.
[6]高有进.6.2米液压支架关键技术研究与优化设计[D].武汉:华中科技大学,2008.
[7]范储旭,刘付营,边彦胜,等.ANSYS-PDS在液压支架前连杆可靠性分析中的应用[J].矿山机械,2015,43(4):14-17.
[8]戚松,赵洪伦,王社峰.运用ANSYS软件与蒙特卡罗法相结合的随机有限元方法进行车辆构件强度可靠性分析[J].铁道车辆,2009,47(2):6-9+47.
[9]韩力群.人工神经网络教程[M].北京:北京邮电大学出版社,2006.
[10]史忠植.神经网络[M].北京:高等教育出版社,2009.
[11]余胜威.MATLAB优化算法案例分析与应用[M].北京:清华大学出版社,2015.