基于贝叶斯网络的某民用飞机着陆距离研究  

Research on landing distance of a civil aircraft based on Bayesian network

作　　者：王可[1] 邵静雯 杨俊[1] WANG Ke;SHAO Jingwen;YANG Jun(Flight Technology College,Civil Aviation Flight University of China,Guanghan 618307,China)

机构地区：[1]中国民用航空飞行学院飞行技术学院,广汉618307

出　　处：《航空工程进展》2025年第2期160-167,共8页Advances in Aeronautical Science and Engineering

基　　金：中央高校基本科研业务费资助项目重点项目(24CAFUC01001);中国民用航空飞行学院研究生创新项目基金(X2023-7);2024四川省科技成果转移转化示范项目(2024ZHCG0003)。

摘　　要：采用民用飞机的实际运行数据和飞机性能软件仿真审定数据对着陆距离进行对比分析,能够为该型飞机运行能力的提升提供量化支撑。从实际运行数据中提取性能仿真计算所需的关键条件以得到相同条件下的所需着陆距离(RLD)及距离差,利用四分位法将着陆相关数据离散化处理,并对建模方法进行比选;采用PC算法学习网络结构,采用贝叶斯估计学习网络参数,构建实际着陆距离超出RLD风险模型;利用贝叶斯网络精确推理计算超出RLD状态的后验概率及最大后验假设。结果表明:拉平至接地时间在7 s左右、接地至地速40 kt(1 kt=1.852 km/h)时间在18~24 s之间可有效降低实际着陆距离超出RLD概率,并结合相关参数对着陆运行提出建议以降低超出RLD风险。Adopting the actual operation data of a domestic civil aircraft and the simulation and validation data of air-craft performance software to perform the landing distance comparison and analysis can provide the quantitative sup-port for the improvement of the aircraft's operation capability.The key conditions required for performance simula-tion calculation are extracted from the actual operation data to obtain the required landing distance(RLD)and dis-tance difference under the same conditions.The landing-related data is discretized by the quartile method,and the modeling methods are compared and selected.The network structure is learned by PC algorithm.Bayesian estima-tion is used to learn network parameters to construct the risk model of actual landing distance exceeding RLD.The posterior probability and maximum posterior hypothesis of exceeding RLD state are calculate by using Bayesian net-work accurate reasoning.The results show that the probability of exceeding RLD can be effectively reduced by flat-tening to ground time of about 7 s and grounding to ground speed of 40 knots between 18~24 s,and some sugges-tions are put forward to reduce the risk of exceeding RLD by combining relevant parameters.

关 键 词：着陆距离 快速存取记录器(QAR)数据 性能软件 贝叶斯网络 运行建议 

分 类 号：V328.3[航空宇航科学与技术—人机与环境工程] V212.131