基于动态贝叶斯网络的民航空中停车事件安全风险评估

doi:10.16265/j.cnki.issn1003-3033.2023.07.1884

摘要/Abstract

摘要：

为探究组织和人的因素对发动机空中停车事件的影响及其演化情况,评估航空公司空停事件的风险水平,基于动态贝叶斯网络(DBN),构建一种适用于民航空停的风险评估模型。首先,运用主动与被动相结合的方式,识别影响发动机发生空停事件的组织和人的因素;其次,利用毕达哥拉斯模糊和决策试验与评估实验室(DEMATEL)方法,探究各个风险因素之间的因果关系;然后,基于得到的风险因素因果关系构建民航空停DBN,依据统计分析和专家经验确定民航空停的DBN参数,通过GeNle概率推理,进一步得出空停事件发生的概率;最后,以S航空公司的CFM56-5B发动机为例,评估民航空停事件安全风险。结果表明:2020—2021年,S航空公司的CFM56-5B发动机发生空停事件的概率分别为1.260×10^-6、1.352×10^-6;导致民航空停事件安全风险变化的主要原因是资源投入减少导致教育培训效果降低进而影响作风意识和合作沟通。

关键词: 动态贝叶斯网络(DBN), 空中停车 (IFSD), 安全风险评估, 毕达哥拉斯模糊, 决策试验与评估实验室(DEMATEL)

Abstract:

In order to explore the impact and evolution of organizational and human factors on IFSD events of engine and to evaluate the risk level of airline IFSD events, a risk assessment model was built for civil aviation IFSD based on DBN. Firstly, the active and passive methods were used to identify the organizational and human factors that affected the occurrence of IFSD. Secondly, Pythagorean fuzzy DEMATEL was used to explore the causal relationship between risk factors. Then, the DBN of civil aviation IFSD was constructed based on the obtained causal relationship, and the parameters were obtained based on statistical analysis and expert experience. The occurrence probability of IFSD events was obtained through GeNle probability reasoning. Finally, the CFM56-5B engine of S Airlines was taken as an example to conduct the safety risk assessment of IFSD events. The results indicate that the probability of air stop of CFM56-5B engine of S Airlines from 2020 to 2021 is 1.260×10^-6 and 1.352×10^-6, respectively. The main reason for the change in safety risks of civil aviation IFSD events is that the decrease in "resource investment" leads to the decrease in the effectiveness of "education and training", which in turn affects "work style awareness" and "cooperation and communication".

Key words: dynamic Bayesian networks (DBN), in flight shut-down (IFSD), risk assessment, Pythagoras fuzzy, decision making trial and evaluation laboratory (DEMATEL)

陈芳, 崔庆敏, 向千秋. 基于动态贝叶斯网络的民航空中停车事件安全风险评估[J]. 中国安全科学学报, 2023, 33(7): 16-23.

CHEN Fang, CUI Qingmin, XIANG Qianqiu. Safety risk assessment of civil aviation air parking events based on DBN[J]. China Safety Science Journal, 2023, 33(7): 16-23.

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语言术语	PFN
很低	(0.15,0.85)
低	(0.25,0.75)
中低	(0.35,0.65)
中	(0.50,0.45)
中高	(0.65,0.35)
高	(0.75,0.25)
很高	(0.85,0.15)

语言变量	概率范围	三角模糊数
极低	0.1	(0.1,0.1,0.2)
低	0.3	(0.2,0.3,0.4)
一般	0.5	(0.4,0.5,0.6)
高	0.7	(0.6,0.7,0.8)
极高	0.9	(0.8,0.9,0.9)

状态	解释	影响因子Q
a	行业最佳水平	0.1
b	优于行业平均水平	0.55
c	行业平均水平	1
d	略低于行业平均水平	4
e	远低于行业平均水平	7
f	行业最糟糕水平	10

类别	风险因素
组织风险因素 A	程序文件A₁ 培训教育A₂ 计划任务A₃ 监督A₄ 资源投入A₅ 安全文化A₆
人的风险因素 B	个人能力B₁ 生理和心理状态B₂ 作风意识B₃ 合作沟通B₄

专家	职务	工作经验/a	教育程度	权重
1	质量部副部长	12	本科	0.45
2	质量部经理	10	本科	0.28
3	大修部经理	7	硕士	0.27