典型机型冲偏出跑道耦合故障模式及风险建模

王菲茵; 袁锦彤; 汪磊

doi:10.3963/j.jssn.1674-4861.2023.06.005

典型机型冲偏出跑道耦合故障模式及风险建模

doi: 10.3963/j.jssn.1674-4861.2023.06.005

王菲茵^{1, 2, 3, ,},
袁锦彤¹,
汪磊^{1, 2, 3}

1.
中国民航大学安全科学与工程学院天津 300300
2.
国家空管运行安全技术重点实验室天津 300300
3.
中国民航航空公司人工智能重点实验室天津 300300

基金项目:

天津市自然科学基金项目 23JCQNJC00230

中国民航局安全能力建设资金项目 KJZ49420210076

天津市教委科研计划项目 2023KJ230

详细信息

通讯作者:
王菲茵（1990—），博士，讲师. 研究方向：飞行安全与人因、民航安保风险等. E-mail: fy_wang@cauc.edu.cn

中图分类号: X949
计量
- 文章访问数: 402
- HTML全文浏览量: 226
- PDF下载量: 99
- 被引次数: 0
出版历程
- 收稿日期: 2023-03-22
- 网络出版日期: 2024-04-03

Coupling Failure Mode and Risk Modeling of Typical Aircrafts Runway Excursion

WANG Feiyin^{1, 2, 3
, ,},
YUAN Jintong¹,
WANG Lei^{1, 2, 3}

1.
College of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China
2.
National Key Laboratory of Air Traffic Operation Safety Technology, Tianjin 300300, China
3.
Key Laboratory of Airlines Artificial Intelligence of Civil Aviation of China, Tianjin 300300, China

摘要

摘要: 冲偏出跑道是国际航空运输协会指出的高风险事件。为了探索冲偏出跑道事件在国际上发生的规律，挖掘冲偏出跑道事件发生的影响因素与其耦合特征，研究了2007—2018年57起典型机型的冲偏出跑道事件的调查报告，对事件发生的伤亡人数、机型、原因等进行分析。针对冲偏出跑道事件影响因素多样且复杂的特点，融合了人因分析与分类系统模型（human factor analysis and classification system，HFACS）、SHELL模型和失效模式和影响分析方法（failuremode and effect analysis，FMEA），弥补了单一方法使用的局限性，基于优化的HFACS模型，纵向深入分析了人为因素在冲偏出跑道事件中的影响，基于改进后的SHELL模型横向全面分析了冲偏出跑道事件中多因素之间的耦合影响，使用FMEA方法对冲偏出跑道事件的多影响因素耦合效应深入挖掘，提出了诱发冲偏出跑道的18种多因素耦合故障模式，判别故障模式的发生度、严重度和预测度，进而量化故障模式的风险优先值。结果表明：91.2%的冲偏出跑道事件发生在着陆阶段；87.7%的冲偏出跑道事件与机组人为因素影响有关，其中对飞机的控制不足发生频次最高，占比31.1%；多因素耦合造成了78.9%的事件发生，多因素耦合故障模式中故障模式F2-1中的机组因素和气象因素的叠加风险优先值最高，为364.8，发生次数占比21.05%也为最高，该故障模式是需要重点防控的对象，说明飞行员需要加强复杂天气条件下的冲偏出跑道防控模拟训练。
- 民航安全 /
- 冲偏出跑道风险 /
- 人为因素 /
- 影响因素挖掘 /
- 耦合故障模式
Abstract: Runway excursion is identified as high-risk event by the International Air Transport Association. To explore the pattern of runway excursion incidents in global civil aviation and to explore the influencing factors and their coupling characteristics, the investigation reports of 57 runway excursion incidents of typical aircraft types from 2007 to 2018 are analyzed from the perspectives of number of casualties, aircraft types and causes of the incidents. The HFACS model and SHELL model are used to compensate for the limitations of using a single method considering the diversity and complexity of influencing factors of runway excursion incidents. Specifically, the HFACS model is optimized and adopted to vertically analyze the influence of human factors in the runway excursion event, change the traditional method of the SHELL model to analyze the coupling influence of multiple factors in the runway excursion event systematically and comprehensively and use the FMEA method to explore the coupling effect of multiple influencing factors in the runway excursion event and find 18 multifactor coupling failure modes that induce the runway excursion. The results showed that the risk priority of the failure modes is quantified by identifying the occurrence, severity, and detection of the failure modes. The results showed that 91.2% of the runway excursion events occurred in the landing phase, and 87.7% of the runway excursion events were related to the crew human influence, among which insufficient control of the aircraft occurred most frequently, accounting for 31.1%. Multi-factor coupling caused 78.9% of the events, and the risk priority value of failure mode F2-1 crew factors and meteorological factors in multi-factor coupling failure mode is the highest at 364.8, with an occurrence rate of 21.05%, which is the object that needs to be focused on prevention and control, indicating that pilots need to strengthen the simulation training of runway excursion under complex weather conditions.
- civil aviation safety /
- runway excursion risk /
- human factors /
- influencing factors mining /
- coupling failure modes

HTML全文

图 1 事件发生历年情况

Figure 1. Events occurring over the years

下载: 全尺寸图片幻灯片

图 2 冲偏出跑道事件类型分布图

内圈为事件次数，外圈为伤亡人数

Figure 2. Distribution of the types of runway excursion events

下载: 全尺寸图片幻灯片

图 3 冲偏出跑道事件发生地及航空器登记国分布图

Figure 3. Location of the runway excursion and the country of registration of the aircraft

下载: 全尺寸图片幻灯片

图 4 HFACS模型

Figure 4. Human factor analysis and classification system

下载: 全尺寸图片幻灯片

图 5 飞行总时长和机型飞行时长分布

Figure 5. Distribution of total flight hours and aircraft type flight hours

下载: 全尺寸图片幻灯片

图 6 多次发生的直接原因

Figure 6. Direct cause of multiple occurrences

下载: 全尺寸图片幻灯片

图 7 改进后SHELL模型

H硬件为Hardwarc S软件为Softwarc
E环境为Environment L人为Liveware

Figure 7. Optimized SHELL model

下载: 全尺寸图片幻灯片

图 8 多因素耦合作用下故障模式关系图

Figure 8. Failure mode relationship diagram under the action of multi-factor coupling

下载: 全尺寸图片幻灯片

表 1 直接原因频次统计

Table 1. Frequency statistics of direct causes

	飞行员直接原因/不安全行为		频次
差错	技能差错	飞行技术不佳	14
		遗漏飞行操作步骤	1
		对飞机的控制不足	28
	决策差错	经验不足导致	8
		缺乏训练导致	9
		外界压力导致	5
		紧急情况导致	3
	知觉差错	误判距离、高度、空速等	2
	知觉差错	视觉差错	1
违规	习惯性违规	多次违反命令、规定、流程	7
		多次飞行前准备不佳	0
		多次违反工作手册	0
		多次未使用或错误使用设备	0
	偶然性违规	偶然发生的违反命令、规定、流程	10
	偶然性违规	偶然违反工作手册	2

下载: 导出CSV

表 2 SHELL模型影响因素统计

Table 2. Frequency statistics of direct causes

诱发因素	事件次数	占比/%
软件	1	1.8
硬件	8	14.0
驾驶舱环境	2	3.5
机场地形环境	24	42.1
气象环境	34	59.6
空域交通环境	2	3.5
机组	50	87.7
其他人	11	19.3

下载: 导出CSV

表 3 故障模式指标评级表

Table 3. Failure Mode Indicator Rating Table

评价项目	等级	评价标准	评分
发生度	极低	无发生概率	1~2
	低	发生概率低	3~4
	中	发生概率中等	5~6
	高	发生概率高	7~8
	很高	发生概率很高	9~10
	极低	预测难度极低	1~2
预测度	低	预测难度低	3~4
	中	预测难度中等	5~6
	高	预测难度高	7~8
严重度	很高	预测难度很高	9~10
	极低	事件后果轻微	1~2
	低	事件后果较轻	3~4
	中	事件后果中等	5~6
	高	事件后果严重	7~8
	很高	事件后果非常严重	9~10

下载: 导出CSV

表 4 SHELL模型故障模式及风险优先值排序

Table 4. SHELL model failure modes and risk priority rankin

故障模式	机组	气象	机场地形	驾驶舱	空域交通	其他人	硬件	软件	占比%	发生度	预测度	严重度	RPN
F2-1	×	×							21.05	7.4	6	8.2	364.080
F2-4		×	×						3.50	7.4	5.8	8.4	360.528
F3-3		×	×			×			3.50	7	6.4	7.8	349.440
F2-2	×		×						7.00	7	5.8	8.4	341.040
F3-1	×	×	×						15.80	7.2	5.6	8.4	338.688
F4-1	×	×	×			×			5.30	5.6	6.8	8.2	312.256
F3-2	×	×				×			3.50	6.4	5.8	7.8	289.536
F3-5	×		×			×			1.75	6.6	5.4	8	285.120
F1-2							×		1.75	5.2	5.8	8.6	259.376
F2-6			×			×			1.75	5.8	5.4	8.2	256.824
F2-3	×						×		5.30	6	5.2	8	249.600
F3-4	×	×			×				1.75	5.8	5.6	7.6	246.848
F2-5	×				×				1.75	4.8	6.4	7.4	227.328
F1-1	×								19.30	5.4	5.4	7.6	221.616
F2-7			×				×		1.75	4.4	5.4	8.4	199.584
F5-1	×	×	×	×			×		1.75	4	5	9.4	188.000
F5-2	×	×		×	×		×		1.75	3.4	5.4	9.6	176.256
F5-3	×	×				×	×	×	1.75	3.4	6	8.2	167.280

下载: 导出CSV

参考文献(24)

[1]	何鹏, 孙瑞山. 全球疫情下的航空安全前沿问题与研究趋势[J]. 交通信息与安全, 2021, 39 (4): 1-8. doi: 10.3963/j.jssn.1674-4861.2021.04.001 HE P, SUN R S. Frontiers and research trends of aviation safety under the global epidemic[J]. Journal of Transport Information and Safety, 2021, 39 (4): 1-8. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.04.001
[2]	International Air Transport Association. 2021 safety report[R]. Montreal: International Air Transport Association, 2022.
[3]	霍志勤. 中国民航运输航空器偏/冲出跑道统计分析[J]. 中国安全生产科学技术, 2012, 8 (7): 127-132. https://www.cnki.com.cn/Article/CJFDTOTAL-LDBK201207029.htm HUO Z Q. Statistical analysis on runway excursion of transport aircraft in China[J]. Journal of Safety Science and Technology, 2012, 8 (7): 127-132. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-LDBK201207029.htm
[4]	孔祥骏. 冲偏出跑道事件预防研究[J]. 中国民用航空, 2009 (9): 34-36. https://www.cnki.com.cn/Article/CJFDTOTAL-MHJJ200909013.htm KONG X J. Study on preventing runway excursion[J]. China Civil Aviation, 2009 (9): 34-36. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-MHJJ200909013.htm
[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. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGMH201906009.htm WANG J N, ZHANG Y H, ZHANG C J. Unsafe control action of runway excursion based on STPA[J]. Journal of Civil Aviation University of China, 2019, 37(6): 46-50. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGMH201906009.htm
[6]	张晓全, 李秋实. 基于WBA方法的冲出跑道事故风险管理研究[J]. 中国安全科学学报, 2012, 22 (11): 67-73. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201211012.htm ZHANG X Q, LI Q S. Study on risk management for overrun based on WBA[J]. China Safety Science Journal, 2012, 22 (11): 67-73. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201211012.htm
[7]	赵宁宁, 赵宇婷. 基于事故树和贝叶斯网络的飞机偏冲出跑道风险分析[J]. 安全与环境学报, 2014, 14 (3): 141-145. https://www.cnki.com.cn/Article/CJFDTOTAL-AQHJ201403033.htm ZHAO N N, ZHAO Y T. Risk analysis of the runway excursion and overrun event based on the fault tree and Bayesian networks[J]. Journal of Safety and Environment, 2014, 14 (3): 141-145. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-AQHJ201403033.htm
[8]	ODISHO E V, TRUONG D, JOSLIN R E. Applying machine learning to enhance runway safety through runway excursion risk mitigation[J]. Journal of Aerospace Information Systems, 2022, 19 (2): 98-112. doi: 10.2514/1.I010972
[9]	ZHANG Y, SUN Y, CHEN Y. Aircraft runway excursion prediction model based on exponential weight[C]. 11^th International Conference on Reliability, Maintainability and Safety (ICRMS), Hangzhou, China: IEEE, 2016.
[10]	CHANG Y H, YANG H H, HSIAO Y J. Human risk factors associated with pilots in runway excursions[J]. Accident Analysis & Prevention, 2016, 94: 227-237.
[11]	OLIVE X, BIEBER P. Quantitative assessments of runway excursion precursors using Mode S data[C]. International Conference for Research in Air Transportation. Barcelona: ICRAT, 2018.
[12]	DI MASCIO P, COSCIOTTI M, FUSCO R, et al. Runway veer-off risk analysis: an international airport case study[J]. Sustainability, 2020, 12 (22): 9360. doi: 10.3390/su12229360
[13]	DISTEFANO N, LEONARDI S. Apriori algorithm for association rules mining in aircraft runway excursions[J]. Civil Engineering and Architecture, 2020, 8 (3): 206-217. doi: 10.13189/cea.2020.080303
[14]	KETABDARI M, TORALDO E, CRISPINO M. Numerical risk analyses of the impact of meteorological conditions on probability of airport runway excursion accidents[C]. Computational Science and Its Applications-ICCSA 2020: 20^th International Conference, Cagliari, Italy: ICCSA, 2020.
[15]	VOROBYEVA O, BARTOK J, ŠIŠAN P, et al. Assessing the contribution of data mining methods to avoid aircraft run-off from the runway to increase the safety and reduce the negative environmental Impacts[J]. International Journal of Environmental Research and Public Health, 2020, 17(3): 796. doi: 10.3390/ijerph17030796
[16]	WANG C, HOLZAPFEL F. Modeling of the aircraft landing behavior for runway excursion and abnormal runway contact analysis[C]. 2018 AIAA Modeling and Simulation Technologies Conference, Kissimmee: AIAA, 2018.
[17]	MAURO R, SHERRY L. Confronting functional complexity failures: the case of a runway excursion in munich[C]. The Human Factors and Ergonomics Society Annual Meeting, Los Angeles: HFES, 2019.
[18]	汪磊, 梁妍, 张楠, 等. 民航典型安保突发事件统计分析与防控策略[J]. 安全, 2022, 43 (7): 13-19, 12. https://www.cnki.com.cn/Article/CJFDTOTAL-ANQU202207002.htm WANG L, LIANG Y, ZHANG N, et al. Statistical analysis and prevention and control strategies of typical emergency in civil aviation safety & security[J]. Safety & Security, 2022, 43 (07): 13-19, 12. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ANQU202207002.htm
[19]	吴耀男, 林雷, 任新温, 等. 1种基于逻辑结构数的改进型FMEA方法[J]. 中国安全科学学报, 2021, 31 (10): 97-104. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK202110017.htm WU Y N, LIN L, REN X W, et al. An improved fmea method based on logical structure number[J]. China Safety Science Journal, 2021, 31 (10): 97-104. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK202110017.htm
[20]	余建星, 范海昭, 陈海成, 等. 海洋立管失效风险因素分析方法及应用[J]. 中国安全科学学报, 2021, 31 (11): 47-53. YU J X, FAN H Z, CHEN H C, et al. Risk factors analysis method of marine riser failure and its application[J]. China Safety Science Journal, 2021, 31 (11): 47-53. (in Chinese)
[21]	DISTEFANO N, LEONARDI S. Aircraft runway excursion features: a multiple correspondence analysis[J]. Aircraft Engineering and Aerospace Technology, 2019, 91 (1): 197-203.
[22]	OKAFOR E G, JEMITOLA P O, SOLADOYE M A. Assessment of runway excursion causal factors and mitigation strategies[J]. Nigerian Journal of Technology, 2018, 37(3): 619-625. doi: 10.4314/njt.v37i3.9
[23]	WANG L, ZHANG J. The effect of psychological risk elements on pilot flight operational performance[J]. Human Factors and Ergonomics in Manufacturing & Service Industries, 2020, 30 (1): 3-13.
[24]	GAO S, WANG L. Effects of mental workload and risk perception on pilots'safety performance in adverse weather contexts[C]. 22^nd International Conference on Human-Computer Interaction, Copenhagen: HCⅡ, 2020.