• 人工智能 •

### 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

1. 中国民航大学 航空工程学院, 天津 300300
• 收稿日期:2020-05-05 修回日期:2020-07-17 出版日期:2020-12-10 发布日期:2020-08-14
• 通讯作者: 张青(1986-),女,天津人,讲师,硕士,主要研究方向:虚拟现实、虚拟仿真。zhangqing.guagua@163.com
• 作者简介:郑岩(1991-),男,天津人,硕士研究生,主要研究方向:虚拟现实、计算机仿真、适航维修
• 基金资助:
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）。

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

1. College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China
• Received:2020-05-05 Revised:2020-07-17 Online:2020-12-10 Published:2020-08-14
• Supported by:
This work is partially supported by the Special Project of Civil Aviation University of China of Fundamental Research Funds for the Central Universities （3122015D011）， the Undergraduate Teaching Quality and Teaching Reform Research Program of Tianjin Colleges and Universities （D02-0820）.

Abstract: 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 322.479 1 \$/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.