液压支架疲劳寿命近似估算
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摘要
针对常规的液压支架寿命近似分析方法需对危险点进行循环加载和获取,导致计算机负载增加的问题,提出了一种基于遗传算法与BP神经网络的寿命估算模型。利用遗传算法的全局搜索性优化BP神经网络,使其不易陷入局部最小点;利用优化后的BP神经网络建立危险点结构参量到疲劳寿命的网络映射模型。针对样本容量和隐含层节点数进行了测试,测试结果表明,样本容量为40、隐含层节点数为7时,模型估算精度较高;液压支架平均寿命估算值为36 456次,与理论值的最大相对误差为5.27%。
In view of problem that conventional life approximate analytical method of hydraulic support needs cyclic loading and acquisition of dangerous points which leads to increase of computer load,a life estimation model based on genetic algorithm and BP neural network was proposed.The BP neural network is optimized by using global search performance of genetic algorithm to avoid falling into the local minimum points.The optimized BP neural network is used to establish network mapping model between structural parameters of the dangerous points and fatigue life.The test results of sample size and number of hidden layer nodes show that the estimation accuracy of the model is high when the sample size is 40 and the number of hidden layer nodes is 7;the average value of estimated life of hydraulic support is 36 456 times,and the maximum relative error with the theoretical value is 5.27%.
In view of problem that conventional life approximate analytical method of hydraulic support needs cyclic loading and acquisition of dangerous points which leads to increase of computer load,a life estimation model based on genetic algorithm and BP neural network was proposed.The BP neural network is optimized by using global search performance of genetic algorithm to avoid falling into the local minimum points.The optimized BP neural network is used to establish network mapping model between structural parameters of the dangerous points and fatigue life.The test results of sample size and number of hidden layer nodes show that the estimation accuracy of the model is high when the sample size is 40 and the number of hidden layer nodes is 7;the average value of estimated life of hydraulic support is 36 456 times,and the maximum relative error with the theoretical value is 5.27%.
引文
[1]刘栋.煤矿液压支架的现状与发展思路[J].煤炭技术,2008,27(5):18-20.
[2]谢锡纯,李晓豁.矿山机械与设备[M].徐州:中国矿业大学出版社,2000.
[3]王国法.工作面支护与液压支架技术理论体系[J].煤炭学报,2014,39(8):1593-1601.
[4]MAKKONEN M.Predicting the total fatigue life in metals[J].International Journal of Fatigue,2009,31(7):1163-1175.
[5]FAN Zhichao,CHEN Xuedong,CHEN Ling,et al.An equivalent strain energy density life prediction model[C]//5th Fracture Mechanics Symposium,Changsha,2007.
[6]赵东波,陆金桂,姚灵灵.液压支架顶梁疲劳寿命的改进神经网络预测[J].矿业研究与开发,2015(12):106-109.
[7]周文俊.液压支架技术性寿命及其评价指标体系的构建[J].科技信息,2013(3):89-90.
[8]王京涛,陆金桂,赵东波.液压支架掩护梁结构优化及疲劳寿命预测[J].矿业研究与开发,2016(9):72-75.
[9]孙权,刘混举,李博.浅埋深工作面液压支架疲劳寿命分析[J].机械工程与自动化,2015(5):131-132.
[10]武晓旭,龚孔成,贾明涛.煤矿事故预测的指数平滑-BP神经网络混合模型研究[J].中国安全生产科学技术,2014,10(9):165-169.
[11]徐丽娜.神经网络控制[M].北京:电子工业出版社,2003.
[12]王江荣,王玥.基于遗传算法自变量降维的神经网络煤矿瓦斯涌出量预测模型[J].煤,2014,23(3):28-32.
[13]吴沧浦.最优控制的理论与方法[M].北京:国防工业出版社,2000.
[14]宁武龙,刘桓龙,彭卫池,等.基于BP神经网络的轴承寿命预测程序设计[J].现代机械,2014(3):7-10.
[15]闫楚良,郝云霄,刘克格.基于遗传算法优化的BP神经网络的材料疲劳寿命预测[J].吉林大学学报(工学版),2014,44(6):1710-1715.
[16]游双.镍氢电池组SOC神经网络估算策略研究[D].天津:天津大学,2007.
[2]谢锡纯,李晓豁.矿山机械与设备[M].徐州:中国矿业大学出版社,2000.
[3]王国法.工作面支护与液压支架技术理论体系[J].煤炭学报,2014,39(8):1593-1601.
[4]MAKKONEN M.Predicting the total fatigue life in metals[J].International Journal of Fatigue,2009,31(7):1163-1175.
[5]FAN Zhichao,CHEN Xuedong,CHEN Ling,et al.An equivalent strain energy density life prediction model[C]//5th Fracture Mechanics Symposium,Changsha,2007.
[6]赵东波,陆金桂,姚灵灵.液压支架顶梁疲劳寿命的改进神经网络预测[J].矿业研究与开发,2015(12):106-109.
[7]周文俊.液压支架技术性寿命及其评价指标体系的构建[J].科技信息,2013(3):89-90.
[8]王京涛,陆金桂,赵东波.液压支架掩护梁结构优化及疲劳寿命预测[J].矿业研究与开发,2016(9):72-75.
[9]孙权,刘混举,李博.浅埋深工作面液压支架疲劳寿命分析[J].机械工程与自动化,2015(5):131-132.
[10]武晓旭,龚孔成,贾明涛.煤矿事故预测的指数平滑-BP神经网络混合模型研究[J].中国安全生产科学技术,2014,10(9):165-169.
[11]徐丽娜.神经网络控制[M].北京:电子工业出版社,2003.
[12]王江荣,王玥.基于遗传算法自变量降维的神经网络煤矿瓦斯涌出量预测模型[J].煤,2014,23(3):28-32.
[13]吴沧浦.最优控制的理论与方法[M].北京:国防工业出版社,2000.
[14]宁武龙,刘桓龙,彭卫池,等.基于BP神经网络的轴承寿命预测程序设计[J].现代机械,2014(3):7-10.
[15]闫楚良,郝云霄,刘克格.基于遗传算法优化的BP神经网络的材料疲劳寿命预测[J].吉林大学学报(工学版),2014,44(6):1710-1715.
[16]游双.镍氢电池组SOC神经网络估算策略研究[D].天津:天津大学,2007.