基于蛙跳退火粒子群算法的民航发动机单元体修理级别决策及成本优化  

CCivil aviation engine module maintenance level decision-making and cost optimization based on annealing frog leaping particle swarm algorithm

作　　者：张青[1] 郑岩 ZHANG Qing;ZHENG Yan(College of Aeronautical Engineering,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学航空工程学院,天津300300

出　　处：《计算机应用》2020年第12期3541-3549,共9页journal of Computer Applications

基　　金：中央高校基本科研业务费中国民航大学专项资助项目(3122015D011);天津市普通高等学校本科教学质量与教学改革研究计划项目(D02-0820)。

摘　　要：针对民航发动机单元体送修工作范围决策及全寿命维修成本优化问题,提出了以返厂时间间隔为变量的基于蛙跳退火粒子群优化算法的发动机单元体修理级别决策及成本优化模型。首先,考虑维修指导手册中的各单元体送修逻辑图及限寿件到寿更换情况,构建了发动机送修成本函数。其次,借助蛙跳退火粒子群优化算法确定了全寿命期间内不同返厂次数的送修成本及各单元体维修等级。最后,通过算例将所提算法与基本粒子群优化算法、退火粒子群优化算法、混合蛙跳优化算法进行对比,分析了不同返厂次数对送修成本及可靠性的影响。实验结果表明,当发动机在全寿命期内进行5次返厂送修时,蛙跳退火粒子群优化算法的成本平均值为322.4791美元/飞行小时,与其他三种优化算法相比成本最优,可为航空公司和大修企业提供送修决策支持。For the problems of scope decision-making of maintenance for civil aviation engine module and cost optimization of full-life maintenance,the engine module maintenance level decision-making and cost optimization model based on annealing frog leaping particle swarm optimization algorithm with return time interval as variable was proposed.Firstly,according to the maintenance logic diagram for each module in maintenance instruction manual and the replacement situation of life-limited parts,the engine shop visit cost function was built.Secondly,by using the annealing frog leaping particle swarm optimization algorithm,the shop visit costs of different return times and the maintenance level for each module in full life time were determined.Finally,based on examples,the proposed algorithm was compared with the basic particle swarm optimization algorithm,annealing particle swarm optimization algorithm and shuffled frog leaping optimization algorithm,and the influence of different return times on maintenance cost and reliability was analyzed.Experimental results indicate that,when the engine has five shop visits in its full life time,the average cost obtained using annealing frog leaping particle swarm optimization algorithm was 322.4791$/flight hour,which was the optimum value compared with those of the other three optimization algorithms.The proposed algorithm can facilitate the shop visit decision-making of airlines and overhaul companies.

关 键 词：民航发动机 单元体 软时限 送修成本优化 限寿件 蛙跳退火粒子群优化算法 

分 类 号：V239[航空宇航科学与技术—航空宇航推进理论与工程] TP181[自动化与计算机技术—控制理论与控制工程]