中国安全科学学报 ›› 2024, Vol. 34 ›› Issue (12): 64-73.doi: 10.16265/j.cnki.issn1003-3033.2024.12.0662

• 安全工程技术 • 上一篇    下一篇

基于模糊贝叶斯和事件树的配对进近碰撞风险研究

卢飞(), 张健, 赵二丽   

  1. 中国民航大学 空中交通管理学院,天津 300300
  • 收稿日期:2024-08-14 修回日期:2024-10-16 出版日期:2024-12-28
  • 作者简介:

    卢 飞 (1984—),男,山东新泰人,博士,副教授,主要从事空中交通运输规划与管理方面的研究。E-mail:

    张健,讲师。

  • 基金资助:
    国家自然科学基金资助(52272356); 中央高校基本业务费自然科学重点项目(3122022101); 中国民航大学配套经费项目(3122023PT06)

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

LU Fei(), ZHANG Jian, ZHAO Erli   

  1. College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China
  • Received:2024-08-14 Revised:2024-10-16 Published:2024-12-28

摘要:

为评估近距平行跑道配对进近的碰撞风险,建立基于模糊贝叶斯和事件树的配对进近碰撞风险模型。首先,描述配对进近程序,采用失效模式与影响分析(FMEA)方法识别配对进近中存在的风险因素;其次,基于识别的风险因素构建配对进近危险接近和控制失效的贝叶斯网络(BN)模型,并使用专家调查权重法和历史数据统计方法确定根节点的概率,引用七级语言变量对部分根节点概率以及中间节点的条件概率进行模糊化赋值,再使用内心法进行去模糊化处理;然后,将先验概率和样本数据导入BN软件进行期望最大化(EM)参数学习,得到危险接近和控制失效的概率;最后,考虑到危险接近和控制失效与碰撞事件发生的时间序列关系,使用事件树分析方法分析配对进近碰撞风险,并分析BN的敏感性。结果表明:危险接近和控制失效分别对飞行员操作水平和维护不当的敏感性最强。当飞行员操作水平差发生的可能性超过12%和维护不当发生可能性超过0.17%时,配对进近运行将满足不了安全目标水平的要求。

关键词: 贝叶斯网络(BN), 事件树, 配对进近, 碰撞风险, 敏感性分析

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

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