摘要
不稳定进近状态易造成典型后果事件。为评估不稳定进近状态下导致的不同后果事件的风险大小,建立定量风险评估模型。对快速存取记录器(quick access recorder,QAR)数据与进近风险进行分析,选择构成不稳定进近的QAR飞行关键参数作为监控指标,使确定的12个监控指标反映不稳定进近的状态。采用Borda序值法对监控指标进行排序,并依据排序结果,计算不稳定进近各监控指标对不稳定进近事件的影响权重,分析不稳定进近可能导致的严重后果,进而确定不稳定进近导致的典型后果事件。基于信息熵理论中的互信息法,分步构建风险评估模型:①整合互信息法和Borda序值法,改进监控指标权重,弥补单独使用互信息法或Borda序值法在权重确定方面的不足;②使用Laplace平滑法处理数据集零频数问题,减少信息损失,对互信息法在小样本数据集这个特定情况下进行必要补充;③增加后果事件关联性考量,调整基础风险值。应用实例验证模型,结果表明:使用A航空公司2019年QAR数据,模型评估得出冲偏出跑道、CFIT与重着陆、空中失控的风险值分别为4.6095,2.0628,0.1468,风险排序与国际航空运输协会公布的数据占比排序一致,模型结果与实际运行情况相符。对比A、B航空公司不同机型和年份数据,模型风险排序一致。模拟4种不同环境下100次实验,风险值趋势与分布具有相同特点:模拟环境与真实环境的风险排序一致情况总体达90%;冲偏出跑道风险因情况变化而波动,空中失控的高风险值可能预示着严重的安全事件,CFIT与重着陆风险波动小、分布均匀、风险较温和且可预测。
Unstable approaches can easily lead to typical consequence events.This study develops a quantitative risk assessment model to evaluate the risks associated with unstable approaches.Quick access recorder(QAR)data and approach risks are analyzed.Key QAR flight parameters indicative of unstable approaches are selected as monitoring indicators.Twelve monitoring indicators are identified to reflect the state of unstable approaches.The Borda count method is used to rank the monitoring indicators.Based on the ranking results,the study calculates how much each monitoring indicator influences unstable approach events.Potential severe consequences of unstable approaches are analyzed to identify typical consequence events.A risk assessment model is constructed based on the mutual information method from information entropy theory,incorporating the following improvements:①The mutual information method and the Borda count method are integrated to define a weight that comprehensively reflects the monitoring indicators.This approach overcomes the limitations of using either method in isolation for weight determination.②Laplace smoothing is utilized to handle the zero-frequency problem in the dataset.Information loss is mitigated,and a necessary complement is provided to the mutual information method,particularly for scenarios characterized by limited sample sizes.③The correlation between consequence events is considered,and the base risk value is adjusted accordingly.The model is validated using a case study.The results show that using QAR data collected from Airline A in 2019,the model assesses the risk values of runway excursion,CFIT and hard landing,and loss of control in-flight as 4.6095,2.0628,and 0.1468,respectively.This risk ranking is consistent with the data proportion ranking published by the International Air Transport Association.Indicating that the model results align with actual operational situations.The model's risk rankings are found to be consistent across different aircraft type and years.This consistency is observed when comparing data from Airlines A and B.One hundred experiments are simulated under four different environments.The results show that the risk value trends and distributions share similar characteristics.The consistency between the risk rankings in the simulated and real environments reaches 90%overall.The risk of runway excursion fluctuates with changing conditions.The high-risk value of loss of control in-flight may indicate a serious safety event.The risk of CFIT and hard landing shows little fluctuation,with a uniform distribution,indicating a moderate and predictable risk.
作者
汪磊
李蕊君
王菲茵
WANG Lei;LI Ruijun;WANG Feiyin(College of Safety Science and Engineering,Civil Aviation University of China,Tianjin 300300,China)
出处
《交通信息与安全》
CSCD
北大核心
2024年第4期21-29,41,共10页
Journal of Transport Information and Safety
基金
国家自然科学基金项目(32071063)资助。
关键词
飞行安全
不稳定进近
风险评估模型
互信息法
QAR数据
flight safety
unstable approach
risk assessment model
mutual information method
QAR data
