留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

机会维修策略下的民机系统维修决策优化模型

李耀华 魏启东 孙世磊

李耀华, 魏启东, 孙世磊. 机会维修策略下的民机系统维修决策优化模型[J]. 机械科学与技术, 2021, 40(5): 808-815. doi: 10.13433/j.cnki.1003-8728.20200116
引用本文: 李耀华, 魏启东, 孙世磊. 机会维修策略下的民机系统维修决策优化模型[J]. 机械科学与技术, 2021, 40(5): 808-815. doi: 10.13433/j.cnki.1003-8728.20200116
LI Yaohua, WEI Qidong, SUN Shilei. Maintenance Decision Optimization Model of Civil Aircraft System under Opportunistic Maintenance[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(5): 808-815. doi: 10.13433/j.cnki.1003-8728.20200116
Citation: LI Yaohua, WEI Qidong, SUN Shilei. Maintenance Decision Optimization Model of Civil Aircraft System under Opportunistic Maintenance[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(5): 808-815. doi: 10.13433/j.cnki.1003-8728.20200116

机会维修策略下的民机系统维修决策优化模型

doi: 10.13433/j.cnki.1003-8728.20200116
基金项目: 

民航科技引领重大专项 MHRD20160105

详细信息
    作者简介:

    李耀华(1974-), 副教授, 博士, 研究方向为复杂工业过程建模和维修工程分析等研究,li_yaohua@sina.com

  • 中图分类号: V267

Maintenance Decision Optimization Model of Civil Aircraft System under Opportunistic Maintenance

  • 摘要: 以系统单位时间费用率和可用度为目标,构建了民机多部件系统不完全预防性维修多目标优化模型。引入机会维修思想,以部件机会维修可靠度阈值作为模型优化变量,将各部件维修方式按照时间相关性和结构相关性进行优化组合。模型采用改进后的自适应变异粒子群算法(Adaptive mutation particle swarm optimization,AM-PSO)求解Pareto最优解集。利用民机某型发动机反推控制和指示系统实际维修数据进行模型验证与分析,结果表明:在有限运行周期内运用本文的多目标优化模型与方法进行预防性维修活动,能有效降低系统维修成本的同时提高其可用度。
  • 图  1  机会维修策略

    图  2  机会维修策略的选择与判断过程

    图  3  算法求解流程图

    图  4  单目标函数与迭代次数的关系

    图  5  w=0.5时V值与迭代次数的关系

    图  6  费用率与权重系数w的关系

    图  7  可用度与权重系数1-w的关系

    表  1  各部件参数

    序号 Cmr Cpm Cpr tmr/h tpm/h tpr/h Rmin
    1 700 200 1 800 5 4 2 0.8
    2 480 160 1 600 6 5 4 0.75
    3 300 100 1 200 6 5 3 0.7
    4 200 50 1 000 4 2 1 0.6
    下载: 导出CSV

    表  2  各部件预防性维修间隔

    序号 预防性维修间隔/h 最优值N
    T1 T2 T3 T4 T5
    1 203 192 185 179 175 4
    2 182 173 167 163 159 4
    3 92 87 84 82 - 3
    4 362 343 331 323 - 3
    下载: 导出CSV

    表  3  不同w值下的多目标模型优化结果

    权重系数 V* C A/% 最优机会维修可靠度阈值ΔRv*
    w 1-w
    0.1 0.9 1.1126 60.20 91.20 (0.128, 0.167, 0.188, 0.208)
    0.2 0.8 1.2068 57.15 90.15 (0.114, 0.153, 0.175, 0.181)
    0.3 0.7 1.2284 55.86 89.54 (0.109, 0.118, 0.166, 0.175)
    0.4 0.6 1.2159 55.65 89.46 (0.107, 0.116, 0.153, 0.169)
    0.5 0.5 1.1912 55.40 89.20 (0.116, 0.081, 0.140, 0.154)
    0.6 0.4 1.1620 55.26 89.15 (0.112, 0.072, 0.142, 0.149)
    0.7 0.3 1.1253 55.10 88.98 (0.110, 0.059, 0.124, 0.137)
    0.8 0.2 1.0862 54.85 88.86 (0.105, 0.048, 0.101, 0.126)
    0.9 0.1 1.0478 54.70 88.50 (0.091, 0.054, 0.088, 0.118)
    下载: 导出CSV

    表  4  预防维修计划方案

    时间/h 部件序号 时间/h 部件序号
    1 2 3 4 1 2 3 4
    92 0 0 1 0 1 119 1 1 2 1
    184 1 1 1 0 1 238 0 0 1 0
    285 0 0 1 0 1 324 1 1 1 0
    372 1 1 2 1 1 413 0 0 1 0
    469 0 0 1 0 1 496 1 1 2 2
    561 1 1 1 0 1 601 0 0 1 0
    660 0 0 1 0 1 677 1 1 1 0
    747 1 1 2 1 1 788 0 0 1 0
    859 0 0 1 0 1 868 2 2 2 1
    943 2 2 1 0 1 964 0 0 1 0
    1 032 0 0 1 0 2 056
    注:0为不进行维修活动; 1为预防性维修; 2为预防性更换。
    下载: 导出CSV
  • [1] NZUKAM C, VOISIN A, LEVRAT E, et al. Opportunistic maintenance scheduling with stochastic opportunities duration in a predictive maintenance strategy[J]. IFAC-PapersOnLine, 2018, 51(11): 453-458 doi: 10.1016/j.ifacol.2018.08.348
    [2] MOKHTAR E H A, CHATEAUNEUF A, LAGGOUNE R. Condition based opportunistic preventive maintenance policy for utility systems with both economic and structural dependencies-application to a gas supply network[J]. International Journal of Pressure Vessels and Piping, 2018, 165: 214-223 doi: 10.1016/j.ijpvp.2018.07.001
    [3] NGUYEN T A T, CHOU S Y. Maintenance strategy selection for improving cost-effectiveness of offshore wind systems[J]. Energy Conversion and Management, 2018, 157: 86-95 doi: 10.1016/j.enconman.2017.11.090
    [4] NGUYEN K A, DO P, GRALL A. Condition-based maintenance for multi-component systems using importance measure and predictive information[J]. International Journal of Systems Science: Operations & Logistics, 2014, 1(4): 228-245 doi: 10.1080/23302674.2014.983582
    [5] ATASHGAR K, ABDOLLAHZADEH H. Reliability optimization of wind farms considering redundancy and opportunistic maintenance strategy[J]. Energy Conversion and Management, 2016, 112: 445-458 doi: 10.1016/j.enconman.2016.01.027
    [6] ZHAO X, LV Z Y, HE Z D, et al. Reliability and opportunistic maintenance for a series system with multi-stage accelerated damage in shock environments[J]. Computers & Industrial Engineering, 2019, 137: 106029 http://www.sciencedirect.com/science/article/pii/S0360835219304887
    [7] ZHANG C, GAO W, YANG T, et al. Opportunistic maintenance strategy for wind turbines considering weather conditions and spare parts inventory management[J]. Renewable Energy, 2019, 133: 703-711 doi: 10.1016/j.renene.2018.10.076
    [8] ZHOU P, YIN P T. An opportunistic condition-based maintenance strategy for offshore wind farm based on predictive analytics[J]. Renewable and Sustainable Energy Reviews, 2019, 109: 1-9 doi: 10.1016/j.rser.2019.03.049
    [9] 王红, 熊律, 何勇, 等. 考虑故障风险的动车组部件机会维修优化策略[J]. 铁道学报, 2019, 41(3): 79-85 doi: 10.3969/j.issn.1001-8360.2019.03.010

    WANG H, XIONG L, HE Y, et al. Optimization of opportunistic maintenance for electric multiple unit component considering failure risk[J]. Journal of the China Railway Society, 2019, 41(3): 79-85 (in Chinese) doi: 10.3969/j.issn.1001-8360.2019.03.010
    [10] 肖红升, 贺德强, 杨严杰, 等. 基于可靠度的列车多部件预防性机会维修策略研究[J]. 铁道科学与工程学报, 2019, 16(4): 1033-1040 https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201904026.htm

    XIAO H S, HE D Q, YANG Y J, et al. Research on the preventive opportunistic maintenance strategy of train multi-components based on reliability[J]. Journal of Railway Science and Engineering, 2019, 16(4): 1033-1040 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201904026.htm
    [11] 刘泽. 民机系统维修间隔优化方法研究[D]. 天津: 中国民航大学, 2016

    LIU Z. Study on maintenance interval optimization method of civil aircraft system[D]. Tianjin: Civil Aviation University of China, 2016 (in Chinese)
    [12] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of ′995 International Conference on Neural Networks. Perth, WA, Australia: IEEE, 1995: 1942-1948
    [13] SHY Y, EBERHART R C. Empirical study of particle swarm optimization[C]//Proceedings of 1999 Congress on Evolutionary Computation-CEC99. Washington, DC, USA: IEEE, 2002: 320-324
    [14] 徐向阳, 韩洵, 艾星, 等. 改进粒子群算法的行星齿轮系统多目标优化研究[J]. 机械科学与技术, 2018, 37(9): 1352-1358 doi: 10.13433/j.cnki.1003-8728.20180068

    XU X Y, HAN X, AI X, et al. Research on multi-objective optimization of planetary gear system with improved particle swarm optimization[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(9): 1352-1358 (in Chinese) doi: 10.13433/j.cnki.1003-8728.20180068
    [15] HU X H, EBERHART R C. Multiobjective optimization using dynamic neighborhood particle swarm optimization[C]//Proceedings of 2002 IEEE Congress on Evolutionary Computation. Honolulu, USA: IEEE, 2002: 1677-1681
  • 加载中
图(7) / 表(4)
计量
  • 文章访问数:  165
  • HTML全文浏览量:  35
  • PDF下载量:  24
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-12-17
  • 刊出日期:  2021-05-01

目录

    /

    返回文章
    返回