Coupling mechanism of air traffic operation safety risk based on N-K-FRAM

doi:10.16265/j.cnki.issn1003-3033.2024.05.1552

Abstract

Abstract:

In order to explore the coupled evolution mechanism of air traffic operation safety risk, clarify the mechanism of coupling and mutation formation in air traffic operation systems based on a combination of the N-K model and FRAM. Firstly, textual data on unsafe incidents was collected. The risk factors involved were categorized, and their historical frequency of occurrence and the coupling relationship between risk factors were obtained. Secondly, the N-K model was used to solve the coupling degree values between air traffic operational risk factors. Finally, based on the output time and accuracy, the variability of the FRAM functional module was quantitatively evaluated, analyzing the coupling mechanism of air traffic operational safety risks, and safety risk analysis was conducted using regional area navigation(RNAV)approach unsafe events and deviation route unsafe events as examples. The results indicate that the evaluation method based on improved FRAM can quantitatively calculate the variability between functional modules in a reasonable and effective manner, weaken the dependence of traditional analysis methods on subjective consciousness, and make the analysis results more objective and scientific.

Key words: air traffic operations, safety risk coupling, N-K model, functional resonance analysis method (FRAM), unsafe events

CLC Number:

X949

LI Yike, ZHANG Honghai, SHI Zongbei, ZHOU Jinlun. Coupling mechanism of air traffic operation safety risk based on N-K-FRAM[J]. China Safety Science Journal, 2024, 34(5): 175-185.

Figures/Tables 15

Fig.1

Fig.2

Table 1

Table 2

Fig.3

Table 3

Fig.4

Table 4

Table 5

Risk change probability of each risk factor in Fl

P_h=0=0.417 9	P_h=1=0.582 1	P_m=0=1.000 0	P_m=1=0.000 0	$P e = 0$ =1.000 0	$P e = 1$ =0.000 0	$P g = 0$ =0.298 5	$P g = 1$ =0.701 5
P_h=0,m=0= 0.417 9	P_h=0,m=1= 0.000 0	P_h=1,m=0= 0.582 1	P_h=1,m=1= 0.000 0	P_h=0,e=0= 0.417 9	$P h = 0, e = 1$ = 0.000 0	P_h=1,e=0= 0.582 1	P_h=1,e=1= 0.000 0
P_h=0,g=0= 0.000 0	P_h=0,g=1= 0.417 9	P_h=1,g=0= 0.298 5	P_h=1,g=1= 0.283 6	P_m=0,e=0= 1.000 0	$P m = 0, e = 1$ = 0.000 0	P_m=1,e=0= 0.000 0	P_m=1,e=1= 0.000 0
P_m=0,g=0= 0.298 5	P_m=0,g=1= 0.701 5	P_m=1,g=0= 0.000 0	P_m=1,g=1= 0.000 0	$P e = 0, g = 0$ = 0.298 5	$P e = 0, g = 1$ = 0.701 5	$P e = 1, g = 0$ = 0.000	$P e = 1, g = 1$ = 0.000
N_h=0,m=0,e=0= 0.417 9	$N h = 0, m = 0, e = 1$ = 0.000 0	N_h=0,m=1,e=0= 0.000 0	N_h=1,m=0,e=0= 0.582 1	N_h=0,m=1,e=1= 0.000	$N h = 1, m = 0, e = 1$ = 0.000	N_h=1,m=1,e=0= 0.000	N_h=1,m=1,e=1= 0.000
P_h=0,m=0,e=0= 0.417 9	$P h = 0, m = 0, e = 1$ = 0.000 0	P_h=0,m=1,e=0= 0.000 0	P_h=1,m=0,e=0= 0.582 1	P_h=0,m=1,e=1= 0.000 0	$P h = 1, m = 0, e = 1$ = 0.000 0	P_h=1,m=1,e=0= 0.000 0	P_h=1,m=1,e=1= 0.000 0
P_h=0,m=0,g=0= 0.000 0	P_h=0,m=0,g=1= 0.417 9	P_h=0,m=1,g=0= 0.000 0	P_h=1,m=0,g=0= 0.298 5	P_h=0,m=1,g=1= 0.000 0	P_h=1,m=0,g=1= 0.283 6	P_h=1,m=1,g=0= 0.000 0	P_h=1,m=1,g=1= 0.000 0
P_h=0,m=1,g=0= 0.124 7	P_h=0,m=0,g=1= 0.293 2	P_h=0,m=1,g=0= 0.000 0	P_h=1,m=0,g=0= 0.173 8	P_h=0,m=1,g=1= 0.000 0	P_h=1,m=0,g=1= 0.408 3	P_h=1,m=1,g=0= 0.000 0	P_h=1,m=1,g=1= 0.000 0
P_h=0,e=0,g=0= 0.000 0	P_h=0,e=0,g=1= 0.417 9	P_h=0,e=1,g=0= 0.000 0	P_h=1,e=0,g=0= 0.298 5	P_h=0,e=1,g=1= 0.000 0	P_h=1,e=0,g=1= 0.283 6	P_h=1,e=1,g=0= 0.000 0	P_h=1,e=1,g=1= 0.000 0
P_m=0,e=0,g=0= 0.298 5	P_m=0,e=0,g=1= 0.417 9	P_m=0,e=1,g=0= 0.000 0	P_m=1,e=0,g=0= 0.000 0	P_m=0,e=1,g=1= 0.000 0	P_m=1,e=0,g=1= 0.000 0	P_m=1,e=1,g=0= 0.000 0	P_m=1,e=1,g=1= 0.000 0
P_h=0,e=0,g=0= 0.124 7	P_h=0,e=0,g=1= 0.293 2	P_h=0,e=1,g=0= 0.000 0	P_h=1,e=0,g=0= 0.173 8	P_h=0,e=1,g=1= 0.000 0	P_h=1,e=0,g=1= 0.408 3	P_h=1,e=1,g=0= 0.000 0	P_h=1,e=1,g=1= 0.000 0
P_m=0,e=0,g=0= 0.298 5	P_m=0,e=0,g=1= 0.701 5	P_m=0,e=1,g=0= 0.000 0	P_m=1,e=0,g=0= 0.000 0	P_m=0,e=1,g=1= 0.000 0	P_m=1,e=0,g=1= 0.000 0	P_m=1,e=1,g=0= 0.000 0	P_m=1,e=1,g=1= 0.000 0

Table 5

Table 6

Table 7

Table 8

Table 9

Table 10

Risk change probability for each risk factors in function⑨ of unsafe deviation from route events

P_h=0=0.203 7	P_h=1=0.796 3	P_m=0=0.407 3	P_m=1=0.592 7	$P e = 0$ =0.518 6	$P e = 1$ =0.481 4	$P g = 0$ =0.389	$P g = 1$ =0.611
P_h=0,m=0= 0.111 1	P_h=0,m=1= 0.092 6	P_h=1,m=0= 0.296 2	P_h=1,m=1= 0.500 1	P_h=0,e=0= 0.129 6	P_h=0,e=1= 0.074 1	P_h=1,e=0= 0.389	P_h=1,e=1= 0.407 3
P_h=0,g=0= 0.166 7	P_h=0,g=1= 0.037 0	P_h=1,g=0= 0.222 3	P_h=1,g=1= 0.57 4	P_m=0,e=0= 0.203 7	P_m=0,e=1= 0.203 6	P_m=1,e=0= 0.314 9	P_m=1,e=1= 0.277 8
P_h=0=0.203 7	P_h=1=0.796 3	P_m=0=0.407 3	P_m=1=0.592 7	$P e = 0$ =0.518 6	$P e = 1$ =0.481 4	$P g = 0$ =0.389	$P g = 1$ =0.611
P_m=0,g=0= 0.185 2	P_m=0,g=1= 0.222 2	P_m=1,g=0= 0.203 7	P_m=1,g=1= 0.388 9	P_e=0,g=0= 0.259 3	P_e=0,g=1= 0.259 3	P_e=1,g=0= 0.129 6	P_e=1,g=1= 0.351 8
N_h=0,m=0,e=0= 0.037 0	N_h=0,m=0,e=1= 0.074 1	N_h=0,m=1,e=0= 0.092 6	N_h=1,m=0,e=0= 0.166 7	N_h=0,m=1,e=1= 0.000 0	N_h=1,m=0,e=1= 0.129 5	N_h=1,m=1,e=0= 0.222 3	N_h=1,m=1,e=1= 0.277 8
P_h=0,m=0,g=0= 0.074 1	P_h=0,m=0,g=1= 0.037 0	P_h=0,m=1,g=0= 0.092 6	P_h=1,m=0,g=0= 0.111 1	P_h=0,m=1,g=1= 0.000 0	P_h=1,m=0,g=1= 0.185 1	P_h=1,m=1,g=0= 0.111 2	P_h=1,m=1,g=1= 0.388 9
P_h=0,e=0,g=0= 0.092 6	P_h=0,e=0,g=1= 0.037 0	P_h=0,e=1,g=0= 0.074 1	P_h=1,e=0,g=0= 0.166 7	P_h=0,e=1,g=1= 0.000 0	P_h=1,e=0,g=1= 0.222 3	P_h=1,e=1,g=0= 0.055 6	P_h=1,e=1,g=1= 0.351 7
P_m=0,e=0,g=0= 0.111 1	P_m=0,e=0,g=1= 0.092 8	P_m=0,e=1,g=0= 0.074 1	P_m=1,e=0,g=0= 0.148 2	P_m=0,e=1,g=1= 0.129 5	P_m=1,e=0,g=1= 0.166 7	P_m=1,e=1,g=0= 0.055 6	P_m=1,e=1,g=1= 0.222 2

Table 10

Table 11

References 33

[1]	陈磊, 焦健, 赵廷弟. 基于模型的复杂系统安全分析综述[J]. 系统工程与电子技术, 2017, 39(6):1287-1291.
	CHEN Lei, JIAO Jian, ZHAO Tingdi. Review for model-based safety analysis of complex safety critical system[J]. Systems Engineering and Electronics, 2017, 39(6):1287-1291.
[2]	CHEREDNICHENKO K, IVANNIKOVA V, SOKOLOVA O, et al. Model of transport safety assessment[J]. Transport, 2023, 38(4):204-213.
[3]	张宏宏, 甘旭升, 孙静娟, 等. 基于STPA-TOPAZ的低空无人机冲突解脱安全性分析[J]. 航空学报, 2022, 43(7):262-274.
	ZHANG Honghong, GAN Xusheng, SUN Jingjuan, et al. Analysis of low altitude UAV conflict resolution safety based on STPA-TOPAZ[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(7):262-274.
[4]	徐远. 基于功能共振模型的通用航空碰撞事故致因分析研究[D]. 成都: 西南交通大学, 2022.
	XU Yuan. Research on the causes analysis of navigation collision accidents based on function resonance model[D]. Chengdu: Southwest Jiaotong University, 2022.
[5]	HOLLNAGEL E. Functional resonance analysis method: modeling of complex social technical system[M]. Burlington: Ashgate, 2013:21-31.
[6]	张玥, 帅斌, 黄文成, 等. 基于FRAM的铁路危险品运输事故演化机制研究[J]. 中国安全科学学报, 2020, 30(2):171-176. doi: 10.16265/j.cnki.issn1003-3033.2020.02.027
	ZHANG Yue, SHUAI Bin, HUANG Wencheng, et al. Accident evolution mechanism of railway dangerous goods transportation based on FRAM[J]. China Safety Science Journal, 2020, 30(2):171-176. doi: 10.16265/j.cnki.issn1003-3033.2020.02.027
[7]	SUJAN M, PICKUP L, VOS M S D, et al. Operationalising FRAM in healthcare: a critical reflection on practice[J]. Safety Science, 2023:DOI:10.1016/j.ssci.2022.105994.
[8]	SUDIARNO A, MAARIJ A M D. System safety assessment of the warehouse operation using functional resonance analysis method and resilience analysis grid[J]. National Public Health Journal, 2023, 18(4):271-278.
[9]	PATRIARCA R, GRAVIOA G D, WOLTJER B, et al. Framing the FRAM: a literature review on the functional resonance analysis method[J]. Safety Science, 2020,129:195-230.
[10]	CONG Kun, JIAO Jian, ZHAO Tingdi. An improved method for FRAM functional variation identification and analysis[J]. Journal of Physics:Conference Series, 2023, 2470(1):DOI:10.1088/1742-6596/2470/1/012005.
[11]	OUYANG Wenjian, GAN Xusheng, WU Yarong, et al. Human factors analysis of the improved FRAM method for take-off quality lateral shift[J]. Applied Sciences, 2023, 13(8):DOI:10.3390/app13085216.
[12]	MASYS A. Radicalization and recruitment: a systems approach to understanding violent extremism-new developments through FRAM[M]. Hershey: IGI Global, 2018:322-348.
[13]	ALBERY S, BORYS D, TEPE S. Advantages for risk assessment: evaluating learnings from question sets inspired by the FRAM and the risk matrix in a manufacturing environment[J]. Safety Science, 2016,89:180-189.
[14]	LI Weijun, HE Min, SUN Yibo, et al. A proactive operational risk identification and analysis framework based on the integration of ACAT and FRAM[J]. Reliability Engineering and System Safety, 2019,186:101-109.
[15]	SAWARAGI T, HORIGUCHI Y, HINA A. Safety analysis of systemic accidents triggered by performance deviation[C]. 2006 SICE-ICASE International Joint Conference Proceedings, 2006: 1778-1781.
[16]	JENSEN A, AVEN T. Hazard/threat identification: using functional resonance analysis method in conjunction with the anticipatory failure determination method[J]. Proceedings of the Institution of Mechanical Engineers, Part O. Journal of Risk and Reliability, 2017, 231(4):383-389.
[17]	FUKUDA K, SAWARAGI T, HORIGUCHI Y, et al. Applying systemic accident model to learn from near-miss incidents of train maneuvering and operation[J]. IFAC Papers OnLine, 2016,49:543-548.
[18]	FRANCA J E M, HOLLNAGEL E, SANTOS E, et al. FRAM AHP approach to analyse offshore oil well drilling and construction focused on human factors[J]. Cognition, Technology and Work, 2020,22:653-665.
[19]	ROSA L V, FRANCA J E M, HADDAD A N, et al. A resilience engineering approach for sustainable safety in green construction[J]. Journal of Sustainable Development of Energy, Water and Environment Systems, 2017, 5(4):480-495.
[20]	SLIM H, NADEAU S. A proposal for a predictive performance assessment model in complex sociotechnical systems combining fuzzy logic and the functional resonance analysis method (FRAM)[J]. American Journal of Industrial and Business Management, 2019, 9(6):1345-1375.
[21]	PATRIARCA R, GRAVIO G D, COSTANTINO F, et al. FRAM to assess performance variability in everyday work: functional resonance in the railway domain[C]. ANNE-SOPHIE N, 7^th REA Symsposium, 2017:141-146.
[22]	PATRIARCA R, FALEGNAMI A, COSTANTINO F, et al. Resilience engineering for socio-technical risk analysis: application in neuro-surgery[J]. Reliability Engineering & System Safety, 2018, 180:321-335.
[23]	PATRIARCA R, GRAVIO G D, COSTANTINO F. A monte carlo evolution of the functional resonance analysis method (FRAM) to assess performance variability in complex systems[J]. Safety Science, 2017, 91:49-60.
[24]	ZINETULLINA A, YANG Ming, KHAKZAD N, et al. Quantitative resilience assessment of chemical process systems using functional resonance analysis method and dynamic Bayesian network[J]. Reliability Engineering and System Safety, 2021:DOI:10.1016/j.ress.2020.107232.
[25]	LEE J, CHUNG H. A new methodology for accident analysis with human and system interaction based on FRAM: case studies in maritime domain[J]. Safety Science, 2018,109:57-66.
[26]	SMOCZYNSKI P, KADZINSKI A, GILL A. Simulating the world described with the functional resonance analysis method[J]. Safety and Reliability-Safe Societies in a Changing World, 2018(6):1247-1252.
[27]	王岩韬, 唐建勋, 赵嶷飞. 航班运行风险因素耦合性分析[J]. 中国安全科学学报, 2017, 27(7):77-81. doi: 10.16265/j.cnki.issn1003-3033.2017.07.014
	WANG Yantao, TANG Jianxun, ZHAO Yifei. Coupling analysis of risk factors in flight operation[J]. China Safety Science Journal, 2017, 27(7):77-81. doi: 10.16265/j.cnki.issn1003-3033.2017.07.014
[28]	方俊, 郭佩文, 朱科, 等. 基于N-K模型的地铁隧道施工安全风险耦合演化分析[J]. 中国安全科学学报, 2022, 32(6):1-9. doi: 10.16265/j.cnki.issn1003-3033.2022.06.2102
	FANG Jun, GUO Peiwen, ZHU Ke, et al. Coupling evolution analysis of subway tunnel construction safety risk based on N-K model[J]. China Safety Science Journal, 2022, 32(6):1-9. doi: 10.16265/j.cnki.issn1003-3033.2022.06.2102
[29]	吴贤国, 吴克宝, 沈梅芳, 等. 基于N-K模型的地铁施工安全风险耦合研究[J]. 中国安全科学学报, 2016, 26(4):96-101.
	WU Xianguo, WU Kebao, SHEN Meifang, et al. Research on coupling of safety risks in metro construction based on N-K model[J]. China Safety Science Journal, 2016, 26(4) :96-101.
[30]	姜宁. 基于风险耦合的交通安全应急管理系统研究[D]. 武汉: 武汉理工大学, 2011.
	JIANG Ning. The research on traffic safety emergency management system based on risk coupling[D]. Wuhan: Wuhan University of Technology, 2011.
[31]	KAUFFMAN S A. The origins of order: self-organization and selection in evolution[M]. New York: Oxford University Press, 1993:517-519.
[32]	MACCHI L, HOLLNAGEL E, LEONHARD J. Resilience engineering approach to safety assessment: an application of FRAM for the MSAW system[C]. Eurocontrol,Air Traffic Management Safety R&D Seminar 2009, 2009: 51-62.
[33]	曾亮. 多层次模糊评估法在民航不安全事件风险评估中的应用[J]. 中国安全科学学报, 2008, 18(1):131-138.
	ZENG Liang. Application of multi-layer fuzzy evaluation method to risk assessment in civil aviation[J]. China Safety Science Journal, 2008, 18(1):131-138.

风险因素	详细内容
人为因素	机务人员:心理感知、行为错误/遗忘/缺少/延迟、计划有误、设备使用不当、身体不适、程序相关知识经验不足等; 机组人员:与理解与解释相关的沟通能力,行为上错误/延迟/遗忘,信息处理/决策判断,设备操作经验不足,心理认知/注意力/个性态度等; 管制人员:语言与口音/通信准确性等沟通问题,不正确行为表现/信息处理、设备使用问题等
机器设备因素	飞机系统:通信系统对讲机故障,防冰/雨/雪系统故障,电力系统故障、起落架系统损坏、飞机控制系统问题、自动飞行系统、数据记录仪、中央警告面板故障等; 飞机结构:挡风玻璃维护不当、尾翼损坏、尾翼维护不当、桁条损坏、短舱结构损坏、座椅损坏等; 飞机动力装置:发动机故障、发动机压缩机磨损、发动机引气系统故障等
环境气象因素	气象环境:晴空湍流,对流湍流,顺风、大风、降雨、闪电等; 物理环境:地面设备、鸟类动物出现、工作空间环境、跑道表面潮湿、跑道表面覆盖雪/雪泥/冰、周边建筑、地形潮湿泥泞等; 运行环境:机场设施(照明等)、航向道信息准确性、交通拥堵、气象服务信息准确性等
组织管理因素	政策管理问题、设备设计问题、培训、文件信息充分性与可用性、文件未记录保存等

准确率	时间特征
准确率	过早	适时	过晚	未发生
精确	U₀₁:U∈(0,0.1]	U₀₂:U=0	U₀₃:U∈(0.1,0.3]	U₀₄:U∈(0.7,0.9]
可接受	U₀₅:U∈(0.1,0.3]	U₀₆:U=0	U₀₇:U∈(0.3,0.5]	U₀₈:U∈(0.9,1)
不精确	U₀₉:U∈(0.3,0.5]	U₁₀:U∈(0.5,0.7]	U₁₁:U∈(0.7,0.9]	U₁₂:U=1

关系	描述
输入	RNAV航路点数据
输出	在有效地FMS飞行计划中验证程序的正确
时间/s	60
前提	FMC飞行计划中的RNAV程序
资源	RNAV进场图
控制	PF和PNF验证程序

P_{h=1,m=0,e=0,g=0}=0.298 5	P_{h=0,m=1,e=0,g=0}=0	P_{h=0,m=0,e=1,g=0}=0	P_{h=0,m=0,e=0,g=1}=0.417 9	P_{h=1,m=1,e=0,g=0}=0
P_{h=1,m=0,e=1,g=0}=0	P_{h=1,m=0,e=0,g=1}=0.283 6	P_{h=0,m=1,e=1,g=0}=0	P_{h=0,m=1,e=0,g=1}=0	P_{h=0,m=0,e=1,g=1}=0
P_{h=1,m=1,e=1,g=0}=0	P_{h=1,m=1,e=0,g=1}=0	P_{h=1,m=0,e=1,g=1}=0	P_{h=0,m=1,e=1,g=1}=0	P_{h=1,m=1,e=1,g=1}=0

P_{h=1,m=0,e=0,g=0}= 0.287 6	P_{h=0,m=1,e=0,g=0}= 0.146 7	P_{h=0,m=0,e=1,g=0}= 0.245 2	P_{h=0,m=0,e=0,g=1}= 0.001 9	P_{h=1,m=1,e=0,g=0}= 0.075 3
P_{h=1,m=0,e=1,g=0}= 0.094 6	P_{h=1,m=0,e=0,g=1}= 0.040 5	P_{h=0,m=1,e=1,g=0}= 0.015 4	P_{h=0,m=1,e=0,g=1}= 0.023 2	P_{h=0,m=0,e=1,g=1}= 0.000 0
P_{h=1,m=1,e=1,g=0}= 0.015 4	P_{h=1,m=1,e=0,g=1}= 0.021 2	P_{h=1,m=0,e=1,g=1}= 0.005 8	P_{h=0,m=1,e=1,g=1}= 0.003 9	P_{h=1,m=1,e=1,g=1}= 0.023 3