Construction and analysis of evolution network of runway excursion events

doi:10.16265/j.cnki.issn1003-3033.2023.12.1893

Abstract

Abstract:

In order to study the formation mechanism of runway excursion events and reduce the probability of events, this paper analyzed the evolution mechanism of runway excursion events based on scenario deduction and complex network theory, and constructed the evolution network model of out-of-runway events. The static characteristics of the weighted directed network were calculated, including the node degree, betweenness centrality, clustering coefficient and network density. Based on the cascading failure theory, the dangerous propagation path of the evolution network of the runway event was analyzed. The results show that the network density of the evolutionary network is 0.13, the average clustering coefficient is 0.404, and the average path length is 1.549, which shows the small-world characteristics of the network. The comprehensive values of large grounding speed of key nodes, throttle rod and engine damage are 0.508 3, 0.491 2 and 0.487 1, respectively. In the critical path of dangerous propagation, the frequency of grounding speed, throttle rod (operation error) and re-flight in each propagation chain is high, which is an important node in dangerous propagation.

Key words: runway excursion, complex network, evolutionary network, risk factors, cascading failure

CHENG Ming, MEI Aoran. Construction and analysis of evolution network of runway excursion events[J]. China Safety Science Journal, 2023, 33(12): 77-84.

Figures/Tables 15

Tab.1

Fig.1

Tab.2

Fig.2

Tab.3

Tab.4

Tab.5

Tab.6

Tab.7

Tab.8

Fig.3

Fig.4

Tab.9

Fig.5

Tab.10

References 20

[1]	印度客机在印度卡利卡特机场降落时冲出跑道摔成两截[EB/OL].(2020-08-10). https://www.cannews.com.cn/2020/0810/308928.shtml.
[2]	西藏航空一客机在重庆机场偏出跑道[EB/OL].(2022-05-12). http://xn.caac.gov.cn/XN_DQYW/202205/t20220512_213230.html.
[3]	中国民航局. 中国航空安全年报(2020年)[R], 2021.
[4]	王洁宁, 张聪俊. 飞机冲偏出跑道人为差错量化分析模型[J]. 安全与环境学报, 2019, 19(1): 106-113.
	WANG Jiening, ZHANG Congjun. A quantification model for controlling human errors resulting from the runway excursion of the aircraft[J]. Journal of Safety and Environment, 2019, 19(1): 106-13.
[5]	王洁宁, 张钰涵, 张聪俊. 基于STPA冲偏出跑道不安全控制行为分析[J]. 中国民航大学学报, 2019, 37(6): 46-50.
	WANG Jiening, ZHANG Yuhan, ZHANG Congjun. Unsafe control action of runway excursion based on STPA[J]. Journal of Civil Aviation University of China, 2019, 37(6): 46-50.
[6]	占欣. 基于QAR数据的冲/偏出跑道风险评估研究[D]. 天津: 中国民航大学, 2019.
	ZHAN Xin. Research on risk evaluation of runway excursion based on QAR data[D]. Tianjin:Civil Aviation University of China, 2019.
[7]	赵宁宁, 尔士玉. 基于BP神经网络模型的飞机起降冲偏出跑道预测研究[J]. 数学的实践与认识, 2020, 50(18): 1-8.
	ZHAO Ningning, ER Shiyu. The prediction of runway overrun and excursion event based on BP neural network[J]. Mathematics in Practice and Theory, 2020, 50(18): 1-8.
[8]	康宗伟. 基于QAR数据和深度学习的民航客机冲出跑道事件预警研究[D]. 重庆: 重庆大学, 2021.
	KANG Zongwei. Research on early warning of airliner runway overrun events based on QAR data and deep learning[D]. Chongqing: ChongqingUniversity, 2021.
[9]	ALBERT R, JEONG H, BARABASI A L. Error and attack tolerance of complex networks[J]. Nature, 2000, 406(6 794): 378-382. doi: 10.1038/35019019
[10]	MOTTER A E, LAI Y C. Cascade-based attacks on complex networks[J]. Physical Review E, 2002, 66(65102): 1-4.
[11]	王正武, 况爱武, 王贺杰. 考虑级联失效的交通网络节点重要度测算[J]. 公路交通科技, 2012, 29(5): 96-120.
	WANG Zhengwu, KUANG Aiwu, WANG Hejie. Calculation of node important degree for traffic network considering cascading failure[J]. Journal of Highway and Transportation Research, 2012, 29(5): 96-120.
[12]	王兴隆, 贺敏, 刘明学. 空中交通CPS级联失效与缓解策略[J]. 北京航空航天大学学报, 2021, 47(12): 2426-2433.
	WANG Xinglong, HE Min, LIU Mingxue. Air traffic CPS cascading failure and mitigation strategy[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(12): 2426-2433.
[13]	DOBSON I, CARRERAS B A, NEWWAN D E. A loading-dependent model of probabilistic cascading failure[J]. Probability in the Engineering and Informational Sciences, 2005, 19(1): 15-32. doi: 10.1017/S0269964805050023
[14]	CRUCITTI P, LATORA V, MARCHIORI M. Model for cascading failures in complex networks[J]. Physical Review E, 2004, 69(4 Pt 2): DOI:10.1103/PhysRevE.69.045104.
[15]	CRANE D. Dictionary of aeronautical terms[M]. New York: Independent Pub Group, 2007:35-95.
[16]	张硕. 基于数据挖掘的告警关联规则研究与设计[D]. 贵阳: 贵州大学, 2021.
	ZHANG Shuo. Research and design of alarm association rules based on data mining[D]. Guiyang: Guizhou University, 2021.
[17]	程明, 梅傲然. 基于关联规则的跑道着陆事件演化网络构建研究[J]. 中国安全生产科学技术, 2022, 18(12): 203-208.
	CHENG Ming, MEI Aoran. Research on construction of evolution network for runway landing event based on association rules[J]. Journal of Safety Science and Technology, 2022, 18(12): 203-208.
[18]	杨景峰, 朱大鹏, 赵瑞琳. 城市轨道交通网络特性与级联失效鲁棒性分析[J]. 计算机工程与应用, 2022, 58(7): 250-258. doi: 10.3778/j.issn.1002-8331.2105-0157
	YANG Jingfeng, ZHU Dapeng, ZHAO Ruilin. Analysis on characteristics of urban rail transit network and robustness of cascading failure[J]. Computer Engineering and Applications, 2022, 58(7): 250-258. doi: 10.3778/j.issn.1002-8331.2105-0157
[19]	吕芳, 张新琳, 王巍, 等. 银行账户交易复杂网络特性分析[J]. 网络与信息安全学报, 2019, 5(4): 99-107.
	LYU Fang, ZHANG Xinlin, WANG Wei, et al. Analyzing the complex properties of bank account transaction network[J]. Chinese Journal of Network and Information Security, 2019, 5(4): 99-107.
[20]	花玲玲. 基于长短期记忆网络和复杂网络的铁路事故致因分析方法研究[D]. 北京: 北京交通大学, 2020.
	HUA Lingling. Research on railway accident causation based on long short term memory network and complex network[D]. Beijing: Beijing Jiaotong University, 2020.

节点类别	风险因素
起始节点 (V₁—V₅)	起飞V₁	目视进近V₂	重新进近V₃	仪表进近V₄	不稳定进近V₅	—
中间节点 (V₆—V₇₂)	空速 V₆	航线训练 V₇	刹车效应 V₈	未注意跑道中线 V₉	天气原因 V₁₀	飞行时间 V₁₁
	侧风 V₁₂	铺筑道面V₁₃	停机坪 V₁₄	着陆距离 V₁₅	接地速度大V₁₆	复飞 V₁₇
	油门杆 V₁₈	下降率大V₁₉	自动驾驶 V₂₀	目视参考 V₂₁	刹车压力 V₂₂	螺旋桨 V₂₃
	方向舵 V₂₄	不对称推力V₂₅	拉平 V₂₆	监控飞行员 V₂₇	机组资源管理 V₂₈	慢车推力 V₂₉
	飞行姿态 V₃₀	气象信息 V₃₁	进近速度 V₃₂	修正措施 V₃₃	放下起落架 V₃₄	空中交通管制 V₃₅
	操作手册 V₃₆	短跑道 V₃₇	机型 V₃₈	着陆滑跑 V₃₉	减速板 V₄₀	航向 V₄₁
	大雨 V₄₂	前轮转向 V₄₃	近地警告 V₄₄	慢车位 V₄₅	跑道湿滑 V₄₆	偏转 V₄₇
	着陆基准速度 V₄₈	发动机损坏 V₄₉	陷入软地面 V₅₀	跑道灯光 V₅₁	云 V₅₂	反推 V₅₃
	能见度低 V₅₄	机场管理 V₅₅	机械故障 V₅₆	顺风 V₅₇	越过跑道入口 V₅₈	机翼断裂 V₅₉
	离地高度 V₆₀	减速率 V₆₁	襟翼位置 V₆₂	风速 V₆₃	风向变化 V₆₄	机身受损 V₆₅
	扰流板 V₆₆	风切变 V₆₇	标准操作程序 V₆₈	情景意识 V₆₉	拉飘 V₇₀	跑道降落 V₇₁
	违规使用手轮 V₇₂	—	—	—	—	—
结果节点 (V₇₃—V₇₈)	冲出跑道 V₇₃	跑道外接地 V₇₄	偏出跑道 V₇₅	起落架损坏 V₇₆	应急撤离 V₇₇	发动机脱落 V₇₈

序号	前项	后项	置信度
1	{拉飘}	{起落架损坏}	1
2	{机翼断裂}	{起落架损坏}	1
3	{放下起落架}	{起落架损坏}	1
4	{机身受损}	{起落架损坏}	1
5	{重新进近}	{起落架损坏}	1
6	{风向变化}	{起落架损坏}	1
7	{下降率大}	{起落架损坏}	1
8	{襟翼位置}	{起落架损坏}	1
9	{离地高度}	{起落架损坏}	0.978 3
10	{下降率大}	{不稳定进近}	0.967 7

特征指标	平均路径长度	网络聚类系数
冲偏出跑道事件演化网络	2.549	0.404
同规模随机网络	1.792	0.132

参数名称	筛选前	筛选后
节点个数	78	76
边数	1 478	739
网络密度(有向)	0.246	0.13
平均度	18.949	9.724
平均加权度	9.757	5.98
网络直径	4	3
平均聚类系数	0.801	0.404
平均路径长度	1.789	2.549

节点	入度	度	度中心性	节点	出度	度	度中心性
起落架损坏	67	70	0.933 3	天气原因	25	27	0.36
接地速度大	59	61	0.813 3	航线训练	24	29	0.386 7
越过跑道入口	49	53	0.706 7	未注意跑道中线	23	33	0.44
机组资源管理	43	46	0.64	风速	23	29	0.386 7
不稳定进近	40	48	0.613 3	襟翼位置	23	26	0.346 7