基于QAR数据与互信息法的进近风险评估模型

汪磊; 李蕊君; 王菲茵

doi:10.3963/j.jssn.1674-4861.2024.04.003

基于QAR数据与互信息法的进近风险评估模型

doi: 10.3963/j.jssn.1674-4861.2024.04.003

中国民航大学安全科学与工程学院天津 300300

基金项目:

国家自然科学基金项目 32071063

详细信息

通讯作者:
汪磊（1982—），博士，研究员. 研究方向：航空安全与人因. E-mail: wanglei0564@hotmail.com

中图分类号: V328
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出版历程
- 收稿日期: 2023-10-17
- 网络出版日期: 2024-11-25

An Assessment Model of Approach Risk Based on QAR Data and Mutual Information Method

College of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China

摘要

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4.609 5，2.062 8，0.146 8，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。
- 飞行安全 /
- 不稳定进近 /
- 风险评估模型 /
- 互信息法 /
- QAR数据
Abstract: 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 4.609 5, 2.062 8, and 0.146 8, 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.
- flight safety /
- unstable approach /
- risk assessment model /
- mutual information method /
- QAR data

HTML全文

图 1 进近风险评估模型构建思路

Figure 1. Methods for developing an approach risk assessment model

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图 2 互信息量计算结果图

Figure 2. Mutual information volume calculations

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表 1 不稳定进近QAR监控指标

Table 1. Unstable approach QAR monitoring indicator

编号	监控指标名称	FOQA监控项目	飞行阶段
i₁	低于下滑道（GLIDE SLOPE）音响警告	ILS下滑道偏离	进近阶段
i₂	下沉率（SINK RATE）大音响警告	下降率大	下降/进近/着陆阶段
i₃	305~152（含）m下降率大	下降率大	进近阶段
i₄	152~15 m下降率大	下降率大	进近阶段
i₅	305 m以下偏离下滑道偏差大	ILS下滑道偏离	进近阶段
i₆	305 m以下偏离航向道偏差大	ILS航向道偏离	进近阶段
i₇	着陆襟翼设置晚	选择着陆襟翼晚	着陆阶段
i₈	放起落架晚	选择着陆构型晚	着陆阶段
i₉	接地前60 s高度低	着陆速度大	着陆阶段
i₁₀	进近305~15 m速度小	进近速度小	进近阶段
i₁₁	进近152~15（含）m速度大	进近速度大	进近阶段
i₁₂	152~60（含）m进近坡度大	进近坡度大	进近阶段

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表 2 不稳定进近QAR监控指标权重

Table 2. Weighting of unstable approach QAR monitoring indicator

编号	监控指标名称	L	S	R_i1	R_i2	b_i	B_i	风险排序	W_i
i₁	低于下滑道(GLIDE SLOPE)音响警告	4	C	1	2	21	0	4	1.0
i₂	下沉率（SINK RATE）大音响警告	3	B	2	1	21	0	4	1.0
i₃	305~152（含）m下降率大	4	C	1	2	21	0	4	1.0
i₄	152~15 m下降率大	3	B	2	1	21	0	4	1.0
i₅	305 m以下偏离下滑道偏差大	4	C	1	2	21	0	4	1.0
i₆	305 m以下偏离航向道偏差大	4	C	1	2	21	0	4	1.0
i₇	着陆襟翼设置晚	2	D	3	3	18	12	1	0.25
i₈	放起落架晚	2	C	3	2	19	11	2	0.50
i₉	接地前60 s高度低	3	C	2	2	20	8	3	0.75
i₁₀	进近305~15 m速度小	4	C	1	2	21	0	4	1.0
i₁₁	进近152~15（含）m速度大	4	C	1	2	21	0	4	1.0
i₁₂	152~60（含）m进近坡度大	3	C	2	2	20	8	3	0.75
注：L为可能性；S为严重性（Doc 9859 SMM）。

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表 3 联合分布频数矩阵

Table 3. Joint distribution frequency matrix

频数	冲偏出跑道	CFIT与重着陆	空中失控
i₁	0	0	-
i₂	0	0	-
i₃	0	-	-
i₄	0	0	-
i₅	2	2	-
i₆	0	0	-
i₇	0	-	-
i₈	0	-	-
i₉	0	0	-
i₁₀	0	-	0
i₁₁	2	-	-
i₁₂	0	0	0
注：“-”为后果事件的监控指标不包含不稳定进近的监控指标。

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表 4 初始联合分布概率

Table 4. Initial joint distribution probability

P(x，y)	冲偏出跑道	CFIT与重着陆	空中失控
i₁	0.066 7	0.066 7	0.000 0
i₂	0.066 7	0.066 7	0.000 0
i₃	0.066 7	0.000 0	0.000 0
i₄	0.066 7	0.066 7	0.000 0
i₅	0.176 5	0.176 5	0.000 0
i₆	0.066 7	0.066 7	0.000 0
i₇	0.066 7	0.000 0	0.000 0
i₈	0.066 7	0.000 0	0.000 0
i₉	0.066 7	0.066 7	0.000 0
i₁₀	0.066 7	0.000 0	0.066 7
i₁₁	0.176 5	0.000 0	0.000 0
i₁₂	0.066 7	0.066 7	0.066 7

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表 5 互信息量

Table 5. Mutual informativity

MI(x，y)	冲偏出跑道	CFIT与重着陆	空中失控
i₁	0.090 5	0.070 4	0.000 0
i₂	0.090 5	0.070 4	0.000 0
i₃	0.067 1	0.000 0	0.000 0
i₄	0.090 5	0.070 4	0.000 0
i₅	0.299 5	0.192 2	0.000 0
i₆	0.090 5	0.070 4	0.000 0
i₇	0.067 1	0.000 0	0.000 0
i₈	0.067 1	0.000 0	0.000 0
i₉	0.090 5	0.070 4	0.000 0
i₁₀	0.090 5	0.000 0	0.044 8
i₁₁	0.178 5	0.000 0	0.000 0
i₁₂	0.113 5	0.083 6	0.049 8

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表 6 互信息量归一化

Table 6. Normalized mutual informativity

W_ih	冲偏出跑道	CFIT与重着陆	空中失控
i₁	0.302 1	0.234 9	0.000 0
i₂	0.302 1	0.234 9	0.000 0
i₃	0.224 2	0.000 0	0.000 0
i₄	0.302 1	0.234 9	0.000 0
i₅	1.000 0	0.641 9	0.000 0
i₆	0.302 1	0.234 9	0.000 0
i₇	0.224 2	0.000 0	0.000 0
i₈	0.224 2	0.000 0	0.000 0
i₉	0.302 1	0.234 9	0.000 0
i₁₀	0.302 1	0.000 0	0.149 6
i₁₁	0.595 9	0.000 0	0.000 0
i₁₂	0.379 1	0.279 0	0.166 3

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表 7 监控指标综合权重

Table 7. Composite weights of monitoring indicators

W_c	冲偏出跑道	CFIT与重着陆	空中失控
i₁	0.302 1	0.234 9	0.000 0
i₂	0.302 1	0.234 9	0.000 0
i₃	0.224 2	0.000 0	0.000 0
i₄	0.302 1	0.234 9	0.000 0
i₅	1.000 0	0.641 9	0.000 0
i₆	0.302 1	0.234 9	0.000 0
i₇	0.056 0	0.000 0	0.000 0
i₈	0.112 1	0.000 0	0.000 0
i₉	0.226 6	0.176 2	0.000 0
i₁₀	0.302 1	0.000 0	0.149 6
i₁₁	0.595 9	0.000 0	0.000 0
i₁₂	0.284 3	0.209 3	0.124 7

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表 8 不同航空公司的不稳定进近QAR监控指标超限事件频数与后果事件调整后风险值

Table 8. The frequency of unstable approach QAR monitoring indicator exceedance events and the risk values of consequence events for different airlines

航空公司	年份	航班量/架次	不稳定进近QAR监控指标i₁~i₁₂超限事件频数/次											冲偏出跑道风险值R_a^’	CFIT与重着陆风险值R_b^’	空中失控风险值R_c^’
航空公司	年份	航班量/架次	i₁	i₂	i₃	i₅	i₆	i₇	i₈	i₉	i₁₀	i₁₁	i₁₂	冲偏出跑道风险值R_a^’	CFIT与重着陆风险值R_b^’	空中失控风险值R_c^’
A	2019	1 486	0	0	0	2	0	0	0	0	0	2	0	4.609 5	2.062 8	0.146 8
A	2020	2 991	0	0	0	2	0	1	0	0	0	2	0	5.098 4	0.195 4	0.167 9
B	2015	8 232	3	0	0	0	0	0	0	0	0	0	0	66.544 3	39.905 3	2.435 6
B	2016	14 667	2	0	0	0	0	1	0	0	0	0	3	132.609 5	19.145 6	9.582 4

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表 9 4种模拟环境下风险值统计分析

Table 9. Statistical analysis of risk values across four simulated environments

统计分析	后果事件类型	Ⅰ	Ⅱ	Ⅲ	Ⅳ
	冲偏出跑道	2.50	6.84	20.86	196.61
风险值平均值	CFIT与重着陆	0.14	0.44	1.30	12.28
	空中失控	0.10	0.21	0.87	7.43
	冲偏出跑道	0.31	1.62	3.97	170.12
风险值标准差	CFIT与重着陆	0.05	0.17	0.49	2.23
	空中失控	0.06	0.22	0.94	15.78
	冲偏出跑道	-0.39	1.60	0.60	1.38
风险值偏度	CFIT与重着陆	-0.16	0.50	0.10	0.63
	空中失控	5.00	5.00	3.66	4.22
	冲偏出跑道	0.72	4.18	3.11	1.38
风险值峰度	CFIT与重着陆	-1.08	0.60	-0.41	1.22
	空中失控	24.99	24.99	15.06	18.96

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表 10 模拟环境与真实环境的风险值排序一致率统计

Table 10. Concordance rate of risk value rankings between simulated and real environments

风险值一致性分析	Ⅰ	Ⅱ	Ⅲ	Ⅳ
真实环境监控指标发生总频数/次	4	5	6	7
模拟环境与真实环境风险值分组排序一致率/%	88	92	88	92
模拟环境与真实环境风险值总体排序一致率/%	90

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参考文献(25)

[1]	王菲茵, 袁锦彤, 汪磊. 典型机型冲偏出跑道耦合故障模式及风险建模[J]. 交通信息与安全, 2023, 41(6): 42-50. doi: 10.3963/j.jssn.1674-4861.2023.06.005 WANG F Y, YUAN J T, WANG L. Coupling failure mode and risk modeling of typical aircrafts runway excursion[J]. Journal of Transport Information and Safety, 2023, 41(6): 42-50. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2023.06.005
[2]	Federal Aviation Administration. Airplane flying handbook: FAA-H-8083-3C[S]. Oklahoma City: Skyhorse Publishing Inc., 2021.
[3]	崔美叶. 进近阶段安全绩效低后果指标研究[D]. 天津: 中国民航大学, 2019. CUI Y M. Research on safety performance low consequence indicators in approaching phase[D]. Tianjin: Civil Aviation University of China, 2019. (in Chinese)
[4]	The International Air Transport Association. Interactive safety report[R/OL]. (2023-09-19)[2023-09-19]. https://www.iata.org/en/publications/safety-report/interactive-safety-report/.
[5]	ZHANG X, MAHADEVAN S. Ensemble machine learning models for aviation incident risk prediction[J]. Decision Support Systems, 2019, 116: 48-63. doi: 10.1016/j.dss.2018.10.009
[6]	孙瑞山, 杨绎煊, 汪磊. QAR数据在飞行安全评价中的应用[J]. 中国安全科学学报, 2015, 25(7): 87-92. SUN R S, YANG Y X, WANG L. Study on flight safety evaluation based on QAR data[J]. China Safety Science Journal, 2015, 25(7): 87-92. (in Chinese)
[7]	孙瑞山, 韩韶华. 基于孤立森林的航班进近超限事件预测[J]. 安全与环境学报, 2022, 22(4): 2010-2016. SUN R S, HAN S H. Ultra limit incident prediction of flight approach based on isolation forest[J]. Journal of Safety and Environment, 2022, 22(4): 2010-2016. (in Chinese)
[8]	王剑辉, 邓伟, 夏正洪, 等. 运输航空飞行安全风险评价方法[J]. 中国安全科学学报, 2019, 29(12): 110-116. WANG J H, DENG W, XIA Z H, et al. Flight risk assessment method of transport aviation[J]. China Safety Science Journal, 2019, 29(12): 110-116. (in Chinese)
[9]	XU L, SHANG J X, ZHENG L J, et al. Curvecluster+: curve clustering for hard landing pattern recognition and risk evaluation based on flight data[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(8): 12811-12821.
[10]	谢嘉仪, 孙华波, 王纯, 等. 精细尺度下的不稳定进近影响因素分析[J]. 武汉大学学报(信息科学版), 2021, 46(8): 1201-1208. XIE J Y, SUN H B, WANG C, et al. Analysis of influence factors for unstable approach in fine-grained scale[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1201-1208. (in Chinese)
[11]	陈农田, 满永政, 李俊辉. 基于QAR数据的民机高高原进近着陆风险评估方法[J]. 北京航空航天大学学报, 2022, 24(6): 1-12. CHEN N T, MAN Y Z, LI J H. Risk assessment method of civil aircraft approach and landing at high plateau based on QAR data[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 24(6): 1-12. (in Chinese)
[12]	崔昊, 张申利, 任海军, 等. 基于卷积神经网络的不稳定进近研究与应用[J]. 航空计算技术, 2023, 53(5): 20-23, 28. doi: 10.3969/j.issn.1671-654X.2023.05.005 CUI H, ZHANG S L, REN H J, et al. Research and application of unstable approach based on convolution a neural network[J]. Aeronautical Computing Technique, 2023, 53(5): 20-23, 28. (in Chinese) doi: 10.3969/j.issn.1671-654X.2023.05.005
[13]	俞力玲. 中国民航飞行品质监控回顾与展望[J]. 中国民用航空, 2012(8): 51-53. YU L L. Review and prospect of carrying out FOQA in China[J]. China Civil Aviation, 2012(8): 51-53. (in Chinese)
[14]	中国民用航空局飞行标准司. 飞行品质监控实施与管理: AC-121/135-FS-2012-45R1[S]. 北京: 中国民用航空局飞行标准司, 2015. Civil Aviation Administration of China Flight Standards Division. Flight quality control implementation and management: AC-121/135-FS-2012-45R1[S]. Beijing: Civil Aviation Administration of China Flight Standards Division, 2015. (in Chinese)
[15]	中国民用航空局. 运行阶段和地面阶段: AC-396-AS-2014-06 [S]. 北京: 中国民用航空局, 2014. Civil Aviation Administration of China. Incident information reporting elements and examples: AC-396-13[S]. Beijing: Civil Aviation Administration of China, 2023. (in Chinese)
[16]	The International Civil Aviation Organization. Safety management manual: Doc 9859[S]. Montréal: The International Civil Aviation Organization, 2018.
[17]	宋昭, 马骋. 基于风险矩阵法与Borda序值法的航空公司运行风险评价讨论[J]. 民航学报, 2022, 6(5): 12-15, 11. doi: 10.3969/j.issn.2096-4994.2022.05.003 SONG Z, MA C, Airline operational risk assessment based on risk matrix method and borda sequence value method[J]. Journal of Civil Aviation, 2022, 6(5): 12-15, 11. (in Chinese) doi: 10.3969/j.issn.2096-4994.2022.05.003
[18]	The International Air Transport Association. Runway safety[R/OL]. (2023-09-19)[2023-09-19]. https://www.iata.org/en/programs/safety/operational-safety/runway-safety/.
[19]	The International Air Transport Association. Loss of control in-flight[R/OL]. (2023-09-19)[2023-09-19]. https://www.iata.org/en/programs/safety/operational-safety/loss-of-control-inflight/.
[20]	The International Air Transport Association. Controlled flight into terrain[R/OL]. (2023-09-19)[2023-09-19]. https://www.iata.org/en/programs/safety/operational-safety/controlledflight-into-terrain/.
[21]	CLAUDE E S. A mathematical theory of communication[J]. The Bell System Technical Journal, 1948, 27(3): 379-423. doi: 10.1002/j.1538-7305.1948.tb01338.x
[22]	STEUER R, KURTHS J, DAUB C O, et al. The mutual information: detecting and evaluating dependencies between variables[J]. Bioinformatics, 2002, 18(2): S231-S240.
[23]	ZHOU Z P, YU X H, ZHU Z Y, et al. Development and application of a bayesian network-based model for systematically reducing safety risks in the commercial air transportation system[J]. Safety Science, 2023, 157(1): 105942.
[24]	ZHAI C X, LAFFERTY J. A study of smoothing methods for language models applied to information retrieval[J]. ACM Transactions on Information Systems, 2004, 22(4): 179-214.
[25]	CHEN, STANLEY F, GOODMAN J. An empirical study of smoothing techniques for language modeling[J]. Computer Speech and Language, 1999, 13(4): 359-394. doi: 10.1006/csla.1999.0128