Research on collision risk of paired approach based on fuzzy Bayesian and event tree

doi:10.16265/j.cnki.issn1003-3033.2024.12.0662

Abstract

Abstract:

In order to assess the collision risk of paired approach to closely spaced parallel runways, a collision risk model based on fuzzy Bayesian and event tree analysis was developed. Initially, the paired approach procedure was delineated, and risk factors inherent in it were identified through the Failure Mode and Effect Analysis (FMEA) method. Subsequently, a Bayesian network model addressing hazards during the paired approach and potential control failures was constructed, leveraging the identified risk factors. The probability of the root node was determined through a combination of expert survey weighting and statistical analysis of historical data. Some root node probobilities and the conditional probability of the intermediate nodes were fuzzified utilizing seven-level linguistic variables, followed by de-fuzzification using the incentre of area. Additionally, priori probabilities and sample data were input into BN software for expectation-maximization(EM)parameter learning, facilitating the determination of hazard proximity and control failure probabilities. Lastly, considering the time series relationship between hazardous approach, control failure, and collision events, the collision risk associated with paired approaches was assessed employing event tree analysis, and a sensitivity analysis was conducted on the BN. The result shows that hazardous approach sensitivity is highest to pilot operating level, while control failure sensitivity is highest to poor maintenance. If the probability of poor pilot performance surpasses 12%, and the likelihood of inadequate maintenance surpasses 0.17%, the paired approach operation fails to meet the safety target level.

Key words: Bayesian network (BN), event tree, paired approach, collision risk, sensitivity analysis

CLC Number:

X949
V35

LU Fei, ZHANG Jian, ZHAO Erli. Research on collision risk of paired approach based on fuzzy Bayesian and event tree[J]. China Safety Science Journal, 2024, 34(12): 64-73.

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FIM故障	飞机性能问题
导航故障	交通流量
ADS-B故障	不利侧风
飞行员经验不足	管制员调配出错
飞行员状态差	外来物撞击
飞行员操作水平差	维护不当
飞行员缺乏情景意识	电力不足
恶劣天气	—

领域专家	权重λ
飞行员	0.35
高校研究人员	0.35
管制员	0.30

序号	语义值	三角模糊数
1	非常高(VH)	(0.8,0.9,1)
2	高(H)	(0.05,0.425,0.8)
3	偏高(MH)	(0.002,0.026,0.05)
4	中等(M)	(0.000 1,0.001 05,0.002)
5	偏低(ML)	(0.000 01,0.000 055,0.000 1)
6	低(L)	(0.000 001,0.000 005 5,0.000 01)
7	非常低(VL)	(0,0.000 000 5,0.000 001)

根节点事件	故障率或失误率
FIM故障	1×10^-6
导航故障	1×10^-6
ADS-B故障	1×10^-6
飞行员经验不足	7.51×10^-3
飞行员状态差	7.62×10^-3
飞行员操作水平差	9.05×10^-3
飞行员缺乏情景意识	9.59×10^-3
恶劣天气	5×10^-4

底事件	飞行员(35%)	高校专家(35%)	管制员(30%)	加权结果
飞机性能问题	VL	VL	VL	(0,0.000 0005,0.000 001)
交通流量	VL	VL	VL	(0,0.000 000 5,0.000 001)
不利侧风	M	M	M	(0.000 1,0.001 05,0.002)
管制员调配出错	VL	L	VL	0.000 000 35,0.000 002 25,0.000 004 15)
外来物撞击	L	ML	VL	(0.000 003 85,0.000 021 325,0.000 0388)
电力不足	VL	VL	VL	(0,0.000 000 5,0.000 001)
维护不当	L	L	VL	(0.000 000 7,0.000 004,0.000 007 3)