Risk-accident emergence and prevention method of high-speed railway system: based on an improved Functional Resonance Analysis Method

Tianfei Li and Wencheng Huang

Reliability Engineering and System Safety, 2025, vol. 264, issue PB

Abstract: An improved Functional Resonance Analysis Method (FRAM) is proposed to analyze the risk-accident emergence of high-speed railway (HSR) system. By integrating Work Breakdown Structure (WBS) and Risk Breakdown Structure (RBS), we decompose operational processes and identify risk factors, constructing a FRAM functional network to model interactions among functional modules. Key innovations include: (i) quantifying functional variability using risk pulse energy, (ii) calculating variability transmission probability via site percolation theory, and (iii) integrating causal emergence theory to link micro-level functional resonance with macro-level accident occurrence. A case study of the 7.23 Ningbo-Wenzhou railway accident validates the method, revealing that four critical functional modules exhibit high resonance intensity due to coupled risk factors, leading to accident emergence. By constructing micro/macro Markov transition matrices and calculating Effective Information (EI), we show that risk-accident emergence occurs when macro-level EI exceeds micro-level EI, indicating nonlinear causal effects in the system. The proposed method enables quantitative analysis of accident evolution pathways and guides targeted prevention: reducing variability transmission probability, enhancing information transmission efficiency, and improving module resistance to risk coupling. These findings provide a data-driven framework for proactive safety management in HSR systems, addressing limitations of traditional qualitative FRAM approaches.

Keywords: Risk-accident emergence and prevention; High-speed railway system; Improved functional resonance analysis method; Causal emergence; Coarse-grained strategy; Effective information (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:264:y:2025:i:pb:s0951832025006386

DOI: 10.1016/j.ress.2025.111438

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