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。Abstract: The research on the maintenance strategy optimization of civil aircraft multi-component series system has important practical significance at this stage. A multi-objective optimization model for incomplete preventive maintenance of civil aircraft multi-component systems was constructed with the system unit time cost rate and availability as the goals. Introducing the opportunistic maintenance strategy, the component opportunistic maintenance reliability threshold is used as a model optimization variable, and the component maintenance methods are optimized and combined according to time correlation and structure correlation. The method employs an improved adaptive mutation particle swarm optimization (AM-PSO) to solve the Pareto optimal solution set. Using the actual maintenance data of an engine′s back-thrusting control and instruction system for civil aircraft for model verification and analysis, the results show that the multi-objective optimization model and method of this paper can effectively reduce system maintenance costs for preventive maintenance activities in a limited operating cycle, and meanwhile can increase availability of civil aircraft.
-
Key words:
- opportunistic maintenance /
- maintenance cost /
- availability /
- AM-PSO
-
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 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 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) 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。 -
[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.1001-8360.2019.03.010WANG 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.htmXIAO 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, 2016LIU 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.1003-8728.20180068XU 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