A Risk Analysis of Human-machine System of Civil Aircraft in Take-off Stage
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摘要: 为了评估民机驾驶舱人机系统在起飞阶段的安全风险，识别危险节点，研究了1种基于节点弹性理论的人机系统风险分析方法。运用层次任务分析法（hierarchical task analysis，HTA）和功能共振事故模型（functional resonance accident model，FRAM），分析人机系统的主要功能，及功能之间的逻辑关系，构建人机系统功能网络模型；再分析功能间和功能内特征的影响因素及连接关系，识别功能失效模式，构建了人机系统风险传播网络模型；引入易感-感染-康复模型（susceptible-infectious-recovered，SIR）模型，模拟风险在系统网络中的动态传播过程。改进认知可靠性和失误分析方法（cognitive reliability and error analysis method，CREAM），识别民机驾驶舱人机系统失效模式和共同绩效条件，并计算了所提SIR模型中的失效概率、传播率和恢复概率。针对风险的动态传播过程，考虑系统受扰和恢复时间，提出1种改进的节点弹性度量方法，更准确地反应风险发生后系统性能变化和弹性表现。以1个起飞阶段民机驾驶舱人机系统为例，①所提方法识别了4种重大风险节点、7种一般风险节点、33种低风险节点以及48种微小风险节点；②在前3类风险节点中，人因失误占比分别为100%、42%及45%；③风险分析表明，包括飞行员疲劳程度和视觉负荷在内的人为因素更易形成安全事故；④分析结果与起飞阶段险情统计分析结果相互印证。此外，所提方法分析了人机系统性能变化过程，发现在系统恢复过程中存在的困难及二次风险的易发倾向。以上结论验证了本文所提方法的有效性，且有助于提出系统风险管理的相应策略。Abstract: To assess the risk of a human-machine system of civil aircraft cockpits during the take-off phase and identify the dangerous nodes, this paper explores a resilience-based risk analysis method. The method employs the hierarchical task analysis (HTA) and the functional resonance accident model (FRAM) to identify the main functions of the human-machine system, and analyze their interconnections, thereby constructing a system function network model. A risk propagation network throughout the system function network is developed by analyzing the intrinsic and extrinsic influencing factors, and a susceptible-infectious-recovered (SIR) model is introduced to simulate the propagation of risk within this network. An improved cognitive reliability and error analysis method (CREAM) is developed to identify system failure modes and common performance conditions, thereby calculating probabilities of failure, transmission, and recovery within the proposed SIR model. Aiming at the dynamic propagation of risk, an enhanced resilience model is developed to accurately reflect system performance and resilience, which considers the timing of system disturbances and recovery. To validate the proposed method, an example of the take-off process is analyzed, and results show that: ①4 major risk nodes, 7 general risk nodes, 33 low-risk nodes, and 48 minimal-risk nodes are identified. ②In the first three categories of nodes, human errors account for 100%, 42%, and 45% respectively. ③Human factors, including pilot fatigue and visual load, are more likely to form incidents. ④These findings are corroborated with the statistical analysis results. Furthermore, the proposed method analyzes the performance change process of the human-machine system, which reveals challenges in system recovery and the tendency towards secondary risks. In summary, conclusions above confirm the effectiveness of the resilience-based analysis method proposed in this paper, emphasizing the need for risk management strategies.
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Key words:
- aviation safety /
- human-machine system /
- risk factors identification /
- resilience /
- SIR model
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图 3 起飞阶段人机系统功能网络模型
Figure 3. Function network model of human-machine system in take-off stage
图 4 起飞阶段人机系统风险传播网络
Figure 4. Risk propagation network of human-machine system in take-off stage
图 6 人机系统风险传播SIR模拟流程图
Figure 6. SIR simulation flow chart of risk propagation in human-machine system
表 1 起飞阶段任务程序HTA分析框架
Table 1. HTA analysis framework of mission in take-off stage
分析步骤 分析内容 分析目的 对起飞阶段任务程序进行分解，为建立系统功能网络模型提供基础 系统边界 飞行员与设备之间的操作、观察和分析动作 目标系统 空客A320的标准起飞程序 总目标 完成客机的起飞操作 阶段划分 推力设定 低于148km/h 185km/h VR速度 正爬升 减推高度 加速高度 各阶段子目标 施加推力 方向控制 决策判断 抬轮 收轮 减小推力 选择襟翼 
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表 3 内在影响因素和外在影响因素
Table 3. Intrinsic and extrinsic influencing factors
编号 内容 编号 内容 编号 内容 编号 内容 I1 油门杆熟悉程度 I9 手臂稳定性 O1 油门杆顺滑程度 O9 塔台及环境信息 I2 气象传感器寿命 I10 E/WD可视化程度 O2 刹车旋钮顺滑程度 O10 规章制度 I3 屏幕素质 I11 综合心理素质 O3 刹车手柄有无异物 O11 起落架系统可靠性 I4 信息处理速度 I12 信息处理能力 O4 外部环境导致传感器失效 O12 设备工作环境 I5 可视化程度 I13 经验 O5 环境光 O13 维修时间 I6 困倦程度 I14 发动机寿命 O6 侧杆顺滑程度 O14 襟翼手柄可靠性 I7 眼疲劳程度 I15 襟翼手柄熟悉程度 O7 机身抖动 I8 侧杆熟悉程度 I16 传感器寿命 O8 侧杆死区和敏感性
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表 4 各功能连接信息及变化情况
Table 4. Function connections and variation
编号 类型 名称 内在影响因素 外在影响因素 风险输出 上游耦合 失效链接 N1 人员 推油门杆到50%N1 I1 O1 a推杆位置与规定推力位置不符 N20 I1/O1-N1.a N3 技术 气象雷达 I2 O4 a气象信息不精确
b感知气象信息过晚— I2/O4-N3.a/N3.b N4 技术 ND I3 I4 I5 O5 a信息显示错误
b信息呈现复杂
c屏幕可视性低N3 N3.b/N3.a/I4-N4.a
I5-N4.b
I3/O5-N4.cN5 人员 判断风速风向 I6 I7 a看错风速风向
b风速风向判断错误
c风速风向判断过迟N4 N4/I6/I7-N5.a
I6-N5.b
N4.b/c-N5.cN8 人员 监控PFD/ND I6 I7 a飞机信号模式错误
b飞机跑道位置不精确
c飞机跑道位置更新过晚
d位置判断错误N4 N23 N23-N8.a
N4.a-N8.b/N8.c
I6/I7-N8.dN13 人员 判断是否中断起飞 I11 I12 I13 O9 O10 a错误判断是否中断起飞
b过早中断起飞
c过晚中断起飞N9 I11/I12/I13-N13.a/N13.b/N13.c
O9/O10-N13.a/N13.b/N13.c
N9-N13.b/N13.cN14 人员 控制俯仰 I9 O6 a俯仰角不正确
b过晚到达规定俯仰姿态
c过早到达规定俯仰速率N9 N12 I9-N14.a
N12.a/N12.c/N9.c-N14.b
O6-N14.c
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表 5 起飞阶段人机系统对应CPC水平及影响指数
Table 5. The CPC level and influence index of the human-machine system in take-off stage
CPC 水平 影响指数 CPC 水平 影响指数 地面支持 高效 -1.8 机组工作管理 有效 -1.8 有效 0 合适 -1 无效 1 可容忍 0 低效 1.8 不合适 1.8 机组培训和经验 经验丰富 -1.8 规程可用性 合适 -1.6 经验有限 0 可接受 0 经验缺失 1.8 不合适 1.6 同时操作数量 能力之内 0 可用时间 足够 -1.4 匹配能力 0 暂时不足 1 超出能力 1.2 长期不足 2.5 人机界面完善性 有效 -1.4 组织完善性 高效 -0.6 合适 -0.6 有效 0 可接受 0 无效 0.6 不合适 1.8 低效 1 工作环境 优越 -0.8 可容忍 0 不合适 1.6
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表 6 人机系统功能失效模式及失效概率基本值
Table 6. Failure mode of human-machine system and basic values of failure probability
功能 失效模式 基本值 编号 内容 o1 观察目标错误 0.001 观察 o2 错误辨识 0.007 o3 未进行观察 0.007 i1 诊断失败 0.2 解释 i2 决策错误 0.01 i3 延迟解释 0.01 计划 p1 优先权错误 0.01 p2 不适当计划 0.01 e1 动作方式错误 0.003 e2 动作时间错误 0.003 执行 e3 动作目标错误 0.000 5 e4 动作顺序错误 0.003 e5 动作遗漏 0.03 系统 m1 对性能或安全较小影响 1×10-5 m2 对性能或安全有重大影响 1×10-9
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表 7 人机系统有向连接传播率
Table 7. Propagation rate of different directed connections of human-machine system
连接 功能 CPC影响指数 传播率 人→人 解释 -0.8 0.155 计划 -1.6 机→人 观察 3.4 0.076 人→机 执行 0.8 0.057 5 机→机 系统 0 1.000 1×10-5
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表 8 人机系统风险传播网络模型部分节点失效概率
Table 8. Examples of failure probability of nodes in risk propagation network model of human-machine system
编号 功能 失效模式 CFP0 CPC指数 HEP 编号 功能 失效模式 CFP0 CPC指数 HEP N1.a 执行 e1 0.003 0.8 0.004 42 N5.b 评价 i1 0.01 -0.8 0.006 79 N2.a 执行 e2 0.000 5 0.8 0.000 736 p2 0.01 -1.6 0.004 61 N2.b 执行 e2 0.003 0.8 0.004 42 N5.c 监视 i3 0.01 -0.8 0.006 79 N2.c 执行 e5 0.03 0.8 0.044 2 p1 0.01 -1.6 0.004 61 N3.a 系统 m1 0.001 0 1×10-5 N6.a 执行 e1 0.003 0.8 0.004 42 N3.b 系统 m2 0.000 1 0 1×10-9 N6.b 执行 e2 0.003 0.8 0.004 42 N4.a 系统 m2 0.000 1 0 1×10-9 N7.a 执行 e1 0.003 0.8 0.004 42 N4.b 扫描 o1 0.001 3.4 0.005 18 N8.a 系统 m1 0.001 0 1×10-5 N4.c 扫描 o1 0.001 3.4 0.005 18 N8.b 系统 m1 0.001 0 1×10-5 N5.a 观察 o2 0.007 3.4 0.036 2 N8.c 系统 m1 0.001 0 1×10-5
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表 9 节点弹性平均值及排序
Table 9. Average value of node resilience and sorting
序号 节点 节点弹性 序号 节点 节点弹性 序号 节点 节点弹性 序号 节点 节点弹性 1 I6 1.91×10-2 24 O1 6.61×10-3 47 N17.c 1.06×10-4 70 N7.a 4.37×10-5 2 I7 1.50×10-2 25 I5 6.50×10-3 48 I13 6.61×10-5 71 N6.a 4.36×10-5 3 N23.c 1.46×10-2 26 I9 6.05×10-3 49 N12.b 4.49×10-5 72 N16.a 4.36×10-5 4 N23.b 1.41×10-2 27 N4.c 5.56×10-3 50 O9 4.46×10-5 73 N16.c 4.36×10-5 5 O5 1.19×10-2 28 N17.b 5.42×10-3 51 N6.b 4.46×10-5 74 N18.c 4.36×10-5 6 I3 1.13×10-2 29 N4.b 5.27×10-3 52 N18.b 4.45×10-5 75 N10.b 4.36×10-5 7 N9.c 1.12×10-2 30 O7 5.19×10-3 53 N20.c 4.45×10-5 76 N2.b 4.35×10-5 8 N9.b 1.08×10-2 31 N17.a 5.18×10-3 54 N15.b 4.44×10-5 77 N19.b 4.35×10-5 9 O6 1.07×10-2 32 N5.b 5.17×10-3 55 N16.b 4.44×10-5 78 N22.b 4.35×10-5 10 N18.a 9.50×10-3 33 N2.c 5.14×10-3 56 O4 4.44×10-5 79 I17 4.34×10-5 11 N11.c 9.19×10-3 34 N14.c 5.02×10-3 57 N13.c 4.43×10-5 80 I10 4.34×10-5 12 N3.b 8.25×10-3 35 N14.b 4.99×10-3 58 N8.d 4.42×10-5 81 N19.a 4.34×10-5 13 N23.a 8.12×10-3 36 I8 4.88×10-3 59 N14.a 4.42×10-5 82 N5.a 4.33×10-5 14 N5.c 8.06×10-3 37 N8.c 4.83×10-3 60 I2 4.41×10-5 83 N22.a 4.33×10-5 15 N11.b 7.98×10-3 38 N8.b 4.80×10-3 61 N2.a 4.41×10-5 84 I15 4.33×10-5 16 N9.a 7.92×10-3 39 N11.a 4.68×10-3 62 N10.c 4.40×10-5 85 I4 4.32×10-5 17 N12.a 7.73×10-3 40 N20.a 4.48×10-3 63 N3.a 4.40×10-5 86 O14 4.31×10-5 18 O11 7.42×10-3 41 N20.b 4.08×10-3 64 N13.b 4.39×10-5 87 I16 4.29×10-5 19 I1 7.42×10-3 42 N4.a 3.05×10-3 65 O13 4.39×10-5 88 N21.b 4.29×10-5 20 O3 7.42×10-3 43 O2 2.96×10-3 66 N15.a 4.38×10-5 89 N13.a 4.28×10-5 21 O8 7.34×10-3 44 I11 2.61×10-4 67 O12 4.37×10-5 90 N10.a 4.28×10-5 22 N1.a 6.96×10-3 45 O10 2.43×10-4 68 I14 4.37×10-5 91 N8.a 4.27×10-5 23 N12.c 6.84×10-3 46 I12 1.26×10-4 69 N21.c 4.37×10-5 92 N21.a 4.27×10-5
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[1] 郑秀梅, 田晓康, 柳青, 等. 通用航空事故致因文本挖掘和社会网络分析[J]. 安全与环境学报, 2024, 24(2): 602-609.ZHENG X M, TIAN X K, LIU Q, et al. Text mining and social network analysis of general aviation accident causes[J]. Journal of Safety and Environment, 2024, 24(2): 602-609. (in Chinese) [2] 王菲茵, 袁锦彤, 汪磊. 典型机型冲偏出跑道耦合故障模式及风险建模[J]. 交通信息与安全, 2023, 41(6): 42-50. doi: 10.3963/j.jssn.1674-4861.2023.06.005WANG F Y, YUAN J T, WANG L. Coupling failure mode and risk modeling of typical aircrafts runway excursion[J]. Journal of Transport Information and Safety, 2023, 41(6): 42-50. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2023.06.005 [3] 许舒婷, 谭文倩, 孙立国, 等. 飞机驾驶员与自动器共享控制系统的原理与方法[J]. 飞行力学, 2022, 40(6): 1-8.XU S T, TAN W Q, SUN L G, et al. Principle and method of shared control system between human pilot and autopilot for aircraft[J]. Flight Dynamics, 2022, 40(6): 1-8. (in Chinese) [4] NADINE M, IVAN S, ALEXANDROS P, et al. Safety and reliability in aviation: a systematic scoping review of normal accident theory, high-reliability theory, and resilience engineering in aviation[J]. Safety Science, 2023, 162: 106097. doi: 10.1016/j.ssci.2023.106097 [5] 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 F, CUI Q M, XIANG Q Q. Safety risk assessment of civil aviation air parking events based on DBN[J]. China Safety Science Journal, 2023, 33(7): 16-23. (in Chinese) [6] WU Y B, ZHANG S G, ZHANG X, et al. Analysis on coupling dynamic effect of human errors in aviation safety[J]. Accident Analysis & Prevention, 2023, 192: 107277. [7] LAI H Y. Breakdowns in team resilience during aircraft landing due to mental model disconnects as identified through machine learning[J]. Reliability Engineering & System Safety, 2023, 237: 109356. [8] ASIMINA VOSKAKI T B, KEITH M. The impact of climate hazards to airport systems: a synthesis of the implications and risk mitigation trends[J]. Transport Reviews, 2023, 43 (4): 652-675. doi: 10.1080/01441647.2022.2163319 [9] YASSIEN Y, EZZELDIN M, MOHAMED M, et al. Air transportation infrastructure robustness assessment for proactive systemic risk management[J]. Journal of Management in Engineering, 2020, 36(4): 4020037. doi: 10.1061/(ASCE)ME.1943-5479.0000789 [10] 张晓全, 吴贵锋. 功能共振事故模型在可控飞行撞地事故分析中的应用[J]. 中国安全生产科学技术, 2011, 7(4): 65-70. doi: 10.3969/j.issn.1673-193X.2011.04.011ZHANG X Q, WU G F. Application of FRAM model in CFIT accident analysis[J]. Journal of Safety Science and Technology, 2011, 7(4): 65-70. (in Chinese) doi: 10.3969/j.issn.1673-193X.2011.04.011 [11] ZHANG X, SUN Y C, ZHANG Y J. A task modeling method of intelligent human-computer interaction in aircraft cockpits based on information load flow[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(6): 5619-5634. doi: 10.1109/TAES.2022.3175187 [12] BAURANOV A, RAKAS J. Bayesian network model of aviation safety: impact of new communication technologies on mid-air collisions[J]. Reliability Engineering & System Safety, 2024, 243: 109905. [13] ZHANG W, LIU K, SHENG W, et al. Critical node identification in active distribution network using resilience and risk theory[J]. IET Generation, Transmission & Distribution, 2020, 14(14): 2771-2778. [14] MOUTSINAS G, GUO W. Node-level resilience loss in dynamic complex networks[J]. Scientific Reports, 2020, 10(1): 3599. doi: 10.1038/s41598-020-60501-9 [15] DUI H, ZHU Y, TAO J. Multi-phased resilience methodology of urban sewage treatment network based on the phase and node recovery importance in IoT[J]. Reliability Engineering & System Safety, 2024, 247: 110130. [16] STANTON N A. Hierarchical task analysis: developments, applications, and extensions[J]. Applied Ergonomics, 2006, 37(1): 55-79. doi: 10.1016/j.apergo.2005.06.003 [17] HOLLNAGEL E. FRAM: the functional resonance analysis method: modelling complex socio-technical systems[M]. London: CRC Press, 2017. [18] GUO Y, JIN Y, HU S, et al. Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN[J]. Reliability Engineering & System Safety, 2023, 229: 108850. [19] QIAO W, MA X, LIU Y, et al. Resilience evaluation of maritime liquid cargo emergency response by integrating FRAM and a BN: a case study of a propylene leakage emergency scenario[J]. Ocean Engineering, 2022, 247: 110584. doi: 10.1016/j.oceaneng.2022.110584 [20] PAN X, DU H T, YU H F. A method for safety analysis of human-machine systems based on dynamic Bayesian simulation[J]. Reliability Engineering & System Safety, 2024, 248: 110152. [21] HENG Y M, WU M G, WEN X X. Identifying key risk factors in air traffic controller workload by seir model[J]. Mathematical Problems in Engineering, 2022(1): 7600754. [22] 施金斌, 刘晓佳, 马国旺, 等. 基于改进认知可靠性与失误分析方法的隧道驾驶人因可靠性分析模型[J]. 科学技术与工程, 2023, 23(25): 10983-10989. doi: 10.12404/j.issn.1671-1815.2023.23.25.10983SHI J B, LIU X J, MA G W, et al. Human reliability analysis model for tunnel driving based on improved CREAM method[J]. Science Technology and Engineering, 2023, 23(25): 10983-10989. (in Chinese) doi: 10.12404/j.issn.1671-1815.2023.23.25.10983 [23] GUO Y D, SUN Y C, YANG X, et al. Flight safety assessment based on a modified human reliability quantification method[J]. International Journal of Aerospace Engineering, 2019(1): 2812173. [24] XU M, LI G Y, CHEN A. Resilience-driven post-disaster restoration of interdependent infrastructure systems under different decision-making environments[J]. Reliability Engineering & System Safety, 2024, 241: 109599. [25] BRUNEAU M, CHANG S E, EGUCHI R T, et al. A framework to quantitatively assess and enhance the seismic resilience of communities[J]. Earthquake Spectra, 2003, 19(4): 733-752. doi: 10.1193/1.1623497 [26] HUANG C Y. Further improving general aviation flight safety: analysis of aircraft accidents during takeoff[J]. The Collegiate Aviation Review International, 2020, 38(1): 88-105. -
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