民机直接维修成本优化模型研究
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摘要
针对民机研制阶段直接维修成本(Direct Maintenance Cost,DMC)优化问题进行研究,建立可靠性、维修性协同优化(Collaborative Optimization,CO)模型。模型以DMC最小化为目标,以可靠性参数MTBUR、MTBF和维修性参数MTPM为设计变量。为了求解模型,将遗传算法与反向传播神经网络组合,通过BP神经网络预测输出,再采用遗传算法寻求最优解。根据设计变量确定网络结构为3-5-1,采用实数编码,通过选择、交叉、变异求解最优解及对应设计变量值。最后以冲洗包和压缩机为例通过Matlab仿真研究,验证协同优化在民机研制中的可行性。
In order to study the direct maintenance cost(DMC)optimization of civil aircraft during the development stage,the Collaborative Optimization Model in regard to reliability and maintainability is established. Minimizing the maintenance cost is set as the target,and reliability parameters MTBUR,MTBF and maintainability parameter MTPM are chosen as the design variables. An improved Genetic Algorithm(GA)combined with Back Propagation(BP)Neural Network is designed to get the optimal solution. Firstly the BP Neural Network is used to get the prediction,and then the GA is used to acquire the optimal value. The network structure is defined as 3-5-1 according to the design vector. Individuals are coded by real and the optimal solution with corresponding design vector values are got through selection,crossover and mutation. Finally,flushing set and compressor are taken as examples,and the effectiveness and applicability of this model are verified through the simulation.
In order to study the direct maintenance cost(DMC)optimization of civil aircraft during the development stage,the Collaborative Optimization Model in regard to reliability and maintainability is established. Minimizing the maintenance cost is set as the target,and reliability parameters MTBUR,MTBF and maintainability parameter MTPM are chosen as the design variables. An improved Genetic Algorithm(GA)combined with Back Propagation(BP)Neural Network is designed to get the optimal solution. Firstly the BP Neural Network is used to get the prediction,and then the GA is used to acquire the optimal value. The network structure is defined as 3-5-1 according to the design vector. Individuals are coded by real and the optimal solution with corresponding design vector values are got through selection,crossover and mutation. Finally,flushing set and compressor are taken as examples,and the effectiveness and applicability of this model are verified through the simulation.
引文
[1]陈勇,吴静敏,左洪福.面向全寿命周期的民机直接维修成本分析和控制[J].航空维修与工程,2006(5):24-27.(Chen Yong,Wu Jing-min,Zuo Hong-fu.Life cycle analysis and control of direct maintenance cost for civil aircraft[J].Aviation Maintenance,2006(5):24-27.)
[2]Wang H,Gao J,Wu H.Direct maintenance cost prediction of civil aircraft[J].Aircraft Engineering and Aerospace Technology:An International Journal,2014,86(5):406-414.
[3]Regattieri A,Giazzi A,Gamberi M.An innovative method to optimize the maintenance policies in an aircraft:general framework and case study[J].Journal of Air Transport Management,2015(44):8-20
[4]吴静敏,左洪福.基于案例推理的直接维修成本预计方法[J].航空学报,2005,26(2):190-194.(Wu Jing-min,Zuo Hong-fu.New method for direct maintenance cost estimating by using CBR[J].Acta Aeronautica Et Astronautica Sinica,2005,26(2):190-194.)
[5]贾宝惠,杨杭.民机隐性功能系统预防性维修策略优化研究[J].机械设计与制造,2016(5):257-260.(Jia Bao-hui,Yang Hang.Research on the preventive maintenance policy optimization of civil aircraft hidden function system[J].Machinery Design&Manufacture,2016(5):257-260.)
[6]赵健,冯俊,邱小明.航空电子设备直接维修成本预计方法[J].航空计算技术,2014,44(2):22-25.(Zhao Jian,Feng Jun,Qiu Xiao-ming.Prediction method for direct maintenance cost of avionics[J].Aeronautical Computing Technique,2014,44(2):22-25.)
[7]白小涛,李为吉.基于近似技术的协同优化方法在机翼设计优化中的应用[J].航空学报,2006,27(5):847-850.(Bai Xiao-tao,Li Wei-ji.Application of collaborative optimization based on approximate methods in wing design optimization[J].Acta Aeronautica Et Astronautica Sinica,2006,27(5):847-850.)
[8]龚春林,谷良贤,袁建平.基于全局优化算法的多学科优化计算构架[J].西北工业大学学报,2009(1):52-56.(Gong Chun-lin,Gu Liang-xian,Yuan Jian-ping.Exploring multidisciplinary design optimization(MDO)architecture based on global optimization algorithm[J].Journal of Northwestern Polytechnical University,2009(1):52-56.)
[9]葛继科,邱玉辉,吴春明.遗传算法研究综述[J].计算机应用研究,2008,25(10):2911-2916.(Ge Ji-ke,Qiu Yu-hui,Wu Chun-ming.Summary of genetic algorithm research[J].Application Research of Computers,2008,25(10):2911-2916.)
[10]崔珊珊.遗传算法的一些改进及其应用[D].合肥:中国科技大学,2010.(Cui Shan-shan.Some improvements of the genetic algorithm and their applications[D].Hefei:University of Science and Technology of China,2010.)
[2]Wang H,Gao J,Wu H.Direct maintenance cost prediction of civil aircraft[J].Aircraft Engineering and Aerospace Technology:An International Journal,2014,86(5):406-414.
[3]Regattieri A,Giazzi A,Gamberi M.An innovative method to optimize the maintenance policies in an aircraft:general framework and case study[J].Journal of Air Transport Management,2015(44):8-20
[4]吴静敏,左洪福.基于案例推理的直接维修成本预计方法[J].航空学报,2005,26(2):190-194.(Wu Jing-min,Zuo Hong-fu.New method for direct maintenance cost estimating by using CBR[J].Acta Aeronautica Et Astronautica Sinica,2005,26(2):190-194.)
[5]贾宝惠,杨杭.民机隐性功能系统预防性维修策略优化研究[J].机械设计与制造,2016(5):257-260.(Jia Bao-hui,Yang Hang.Research on the preventive maintenance policy optimization of civil aircraft hidden function system[J].Machinery Design&Manufacture,2016(5):257-260.)
[6]赵健,冯俊,邱小明.航空电子设备直接维修成本预计方法[J].航空计算技术,2014,44(2):22-25.(Zhao Jian,Feng Jun,Qiu Xiao-ming.Prediction method for direct maintenance cost of avionics[J].Aeronautical Computing Technique,2014,44(2):22-25.)
[7]白小涛,李为吉.基于近似技术的协同优化方法在机翼设计优化中的应用[J].航空学报,2006,27(5):847-850.(Bai Xiao-tao,Li Wei-ji.Application of collaborative optimization based on approximate methods in wing design optimization[J].Acta Aeronautica Et Astronautica Sinica,2006,27(5):847-850.)
[8]龚春林,谷良贤,袁建平.基于全局优化算法的多学科优化计算构架[J].西北工业大学学报,2009(1):52-56.(Gong Chun-lin,Gu Liang-xian,Yuan Jian-ping.Exploring multidisciplinary design optimization(MDO)architecture based on global optimization algorithm[J].Journal of Northwestern Polytechnical University,2009(1):52-56.)
[9]葛继科,邱玉辉,吴春明.遗传算法研究综述[J].计算机应用研究,2008,25(10):2911-2916.(Ge Ji-ke,Qiu Yu-hui,Wu Chun-ming.Summary of genetic algorithm research[J].Application Research of Computers,2008,25(10):2911-2916.)
[10]崔珊珊.遗传算法的一些改进及其应用[D].合肥:中国科技大学,2010.(Cui Shan-shan.Some improvements of the genetic algorithm and their applications[D].Hefei:University of Science and Technology of China,2010.)