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。
To assess the risk of a human-machine system of civil aircraft cockpits during the take-off phase and iden-tify the dangerous nodes,this paper explores a resilience-based risk analysis method.The method employs the hier-archical 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%respec-tively.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 to-wards 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.
作者
王逸凡
孙有朝
刘勋
揭裕文
WANG Yifan;SUN Youchao;LIU Xun;JIE Yuwen(College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;Shanghai Aircraft Airworthiness Certification Center of CAAC,Shanghai 200335,China)
出处
《交通信息与安全》
CSCD
北大核心
2024年第4期42-52,共11页
Journal of Transport Information and Safety
基金
国家自然科学基金委员会与中国民用航空局民航联合基金项目(U2033202,U1333119)
国家自然科学基金项目(52172387)
中央高校基本科研业务费(ILA220321A23)资助。
关键词
民航安全
人机系统
风险因素识别
弹性
SIR模型
aviation safety
human-machine system
risk factors identification
resilience
SIR model
