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Using N-K Model to quantitatively calculate the variability in Functional Resonance Analysis Method

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  • Huang, Wencheng
  • Yin, Dezhi
  • Xu, Yifei
  • Zhang, Rui
  • Xu, Minhao

Abstract

In this paper, we formulate the formation mechanism of variability of Functional Resonance Analysis Method (FRAM) based on the risk pulse theory for the first time. The coupling and interactions among human errors, mechanical failures, terrible environment and organization factors might cause the system state change, and cause the variability during the operation processes of the system. Based on the collected historical occurrence frequency of each risk factor and the determined coupling and interaction relationships among the risk factors, the N-K model is used to calculate the coupling risk intensity, which provides a quantitative method to evaluate the variability of the functional module. The more frequent the coupling times, the greater the probability of the coupling. The greater the coupling intensity, the greater the risk and the greater the occurrence probability of accidents. Compared with previous improvement works, the historical statistical frequency of each risk factor is need during the calculation based on N-K model, the results are not influenced by experts. A case study is conducted by taking a railway dangerous goods transportation accident in 2001 as the background. The results show that N-K model is able to calculate the variability of the functional module, quantitatively and effectively.

Suggested Citation

  • Huang, Wencheng & Yin, Dezhi & Xu, Yifei & Zhang, Rui & Xu, Minhao, 2022. "Using N-K Model to quantitatively calculate the variability in Functional Resonance Analysis Method," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    • Handle: RePEc:eee:reensy:v:217:y:2022:i:c:s0951832021005603
    DOI: 10.1016/j.ress.2021.108058
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    References listed on IDEAS

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    7. Kim, Yoo Chan & Yoon, Wan Chul, 2021. "Quantitative representation of the functional resonance analysis method for risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    8. Zinetullina, Altyngul & Yang, Ming & Khakzad, Nima & Golman, Boris & Li, Xinhong, 2021. "Quantitative resilience assessment of chemical process systems using functional resonance analysis method and Dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
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