基于QAR数据的民机高高原进近着陆风险评估方法  

Risk assessment method for civil aircraft approach and landing at high plateau based on QAR data

作　　者：陈农田[1] 满永政 李俊辉 CHEN Nongtian;MAN Yongzheng;LI Junhui(College of Aviation Engineering,Civil Aviation Flight University of China,Guanghan 618307,China;College of Civil Aviation Safety Engineering,Civil Aviation Flight University of China,Guanghan 618307,China)

机构地区：[1]中国民用航空飞行学院航空工程学院,广汉618307 [2]中国民用航空飞行学院民航安全工程学院,广汉618307

出　　处：《北京航空航天大学学报》2024年第1期77-85,共9页Journal of Beijing University of Aeronautics and Astronautics

基　　金：国家自然科学基金民航联合基金重点项目(U2033202);四川省科技厅重点研发项目(2022YFG0213)。

摘　　要：民机高高原进近着陆是高原飞行高风险阶段。为有效实施高高原进近着陆风险识别和等级判据,提出基于熵权可变模糊识别的长短时记忆网络与深度神经网络(LSTM-DNN)相融合的深度学习风险评估方法。基于快速存取记录器(QAR)记录的高高原飞行数据,借鉴民机飞行品质监控(FOQA)咨询通告和行业QAR监控标准,结合指标重要度分析与Delphi专家调查,提取着陆时航向变化大、航迹低、610~305 m进近时下降率大、接地时垂直加速度及153~15 m进近时下降率大5个关键监控项目作为民机高高原进近着陆风险评估指标。为克服评估指标权重主观性偏差,应用熵权法确定评估指标权重,基于可变模糊识别方法构建风险等级隶属函数,建立基于LSTM-DNN的民机高高原进近着陆风险评估模型。以成都—拉萨进近着陆航段为例,提取QAR数据,对该风险评估模型进行训练与测试,并与Logistic多元回归、支持向量机(SVM)等评估方法进行比较,结果表明:所提方法平均准确率达到94.18%,最高可达94.79%,验证了方法的客观有效性。The high plateau approach and landing of civil aircraft is a high-risk stage of high plateau flight.To effectively identify the risk and its grade of this approach and landing,a long short term memory-deep neural network(LSTM-DNN)deep learning risk assessment method is proposed based on the variable fuzzy identification of entropy weights.This method utilizes high-altitude flight data recorded by the quick access recorder(QAR),referencing the advisory notices from the flight operations quality assurance(FOQA)of civil aviation as well as the industry QAR monitoring standards.The method combines indicator importance analysis with Delphi expert surveys to extract five key monitoring items for civil aviation high-altitude approach and landing risk assessment,including significant changes in heading during landing,low trajectory,large descent rate during the 610−305 m approach,touchdown vertical acceleration during landing,and high descent rate during the 153−15 m approach.To overcome the subjective bias of the evaluation index weight,the entropy weight method is then used to determine the evaluation index weight,with the risk level membership function constructed based on the variable fuzzy identification method.Finally,the LSTM-DNN risk assessment model for civil aircraft approach and landing at high plateau is established.Taking the Chengdu−Lhasa approach and landing segment as an example,this study extracted the QAR data to train and test the risk assessment model,and compared the results with those of the evaluation methods such as Logistic multiple regression and support vector machines(SVM).The results show that the recognition rate of the proposed method reaches 94.18%on average with the highest being 94.79%,verifying the effectiveness of the method.

关 键 词：高高原飞行 快速存取记录器数据 熵权 可变模糊识别 LSTM-DNN深度学习 风险评估 

分 类 号：V328.5[航空宇航科学与技术—人机与环境工程]