Volume 42 Issue 4
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WANG Yifan, SUN Youchao, LIU Xun, JIE Yuwen. A Risk Analysis of Human-machine System of Civil Aircraft in Take-off Stage[J]. Journal of Transport Information and Safety, 2024, 42(4): 42-52. doi: 10.3963/j.jssn.1674-4861.2024.04.005
Citation: WANG Yifan, SUN Youchao, LIU Xun, JIE Yuwen. A Risk Analysis of Human-machine System of Civil Aircraft in Take-off Stage[J]. Journal of Transport Information and Safety, 2024, 42(4): 42-52. doi: 10.3963/j.jssn.1674-4861.2024.04.005
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A Risk Analysis of Human-machine System of Civil Aircraft in Take-off Stage

doi: 10.3963/j.jssn.1674-4861.2024.04.005
  • 1.

    College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

  • 2.

    Shanghai Aircraft Airworthiness Certification Center of CAAC, Shanghai 200335, China

  • Received Date: 2024-04-04
    Available Online: 2024-11-25
    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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