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Interdisciplinary safety analysis of complex socio-technological systems based on the functional resonance accident model: An application to railway trafficsupervision

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  • Belmonte, Fabien
  • Schön, Walter
  • Heurley, Laurent
  • Capel, Robert

Abstract

This paper presents an application of functional resonance accident models (FRAM) for the safety analysis of complex socio-technological systems, i.e. systems which include not only technological, but also human and organizational components. The supervision of certain industrial domains provides a good example of such systems, because although more and more actions for piloting installations are now automatized, there always remains a decision level (at least in the management of degraded modes) involving human behavior and organizations. The field of application of the study presented here is railway traffic supervision, using modern automatic train supervision (ATS) systems. Examples taken from railway traffic supervision illustrate the principal advantage of FRAM in comparison to classical safety analysis models, i.e. their ability to take into account technical as well as human and organizational aspects within a single model, thus allowing a true multidisciplinary cooperation between specialists from the different domains involved.

Suggested Citation

  • Belmonte, Fabien & Schön, Walter & Heurley, Laurent & Capel, Robert, 2011. "Interdisciplinary safety analysis of complex socio-technological systems based on the functional resonance accident model: An application to railway trafficsupervision," Reliability Engineering and System Safety, Elsevier, vol. 96(2), pages 237-249.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:2:p:237-249
    DOI: 10.1016/j.ress.2010.09.006
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    Cited by:

    1. David Barilla & Giuseppe Caristi & Tiziana Ciano, 2024. "Cost-benefit risk analysis modeling for corporate compliance: evidence from Italy obtained through investment and industry 4.0 tax credit data analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4459-4478, October.
    2. Patriarca, Riccardo & Falegnami, Andrea & Costantino, Francesco & Bilotta, Federico, 2018. "Resilience engineering for socio-technical risk analysis: Application in neuro-surgery," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 321-335.
    3. Liu, Jintao & Schmid, Felix & Zheng, Wei & Zhu, Jiebei, 2019. "Understanding railway operational accidents using network theory," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 218-231.
    4. Lijie, Chen & Tao, Tang & Xianqiong, Zhao & Schnieder, Eckehard, 2012. "Verification of the safety communication protocol in train control system using colored Petri net," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 8-18.
    5. Liu, Jintao & Schmid, Felix & Li, Keping & Zheng, Wei, 2021. "A knowledge graph-based approach for exploring railway operational accidents," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    6. Steen, Riana & Ferreira, Pedro, 2020. "Resilient flood-risk management at the municipal level through the lens of the Functional Resonance Analysis Model," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    7. Righi, Angela Weber & Saurin, Tarcisio Abreu & Wachs, Priscila, 2015. "A systematic literature review of resilience engineering: Research areas and a research agenda proposal," Reliability Engineering and System Safety, Elsevier, vol. 141(C), pages 142-152.
    8. Patriarca, Riccardo & Bergström, Johan & Di Gravio, Giulio, 2017. "Defining the functional resonance analysis space: Combining Abstraction Hierarchy and FRAM," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 34-46.
    9. Felipe Aguirre & Mohamed Sallak & Walter Schön & Fabien Belmonte, 2013. "Application of evidential networks in quantitative analysis of railway accidents," Journal of Risk and Reliability, , vol. 227(4), pages 368-384, August.
    10. Patriarca, Riccardo & Ramos, Marilia & Paltrinieri, Nicola & Massaiu, Salvatore & Costantino, Francesco & Di Gravio, Giulio & Boring, Ronald Laurids, 2020. "Human reliability analysis: Exploring the intellectual structure of a research field," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    11. Zhou, Jian-Lan & Lei, Yi & Chen, Yang, 2019. "A hybrid HEART method to estimate human error probabilities in locomotive driving process," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 80-89.
    12. Liu, Jintao & Chen, Keyi & Duan, Huayu & Li, Chenling, 2024. "A knowledge graph-based hazard prediction approach for preventing railway operational accidents," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    13. Praetorius, Gesa & Hollnagel, Erik & Dahlman, Joakim, 2015. "Modelling Vessel Traffic Service to understand resilience in everyday operations," Reliability Engineering and System Safety, Elsevier, vol. 141(C), pages 10-21.
    14. Bjerga, Torbjørn & Aven, Terje & Zio, Enrico, 2016. "Uncertainty treatment in risk analysis of complex systems: The cases of STAMP and FRAM," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 203-209.
    15. Li, Zhongping & Cui, Lirong & Chen, Jianhui, 2018. "Traffic accident modelling via self-exciting point processes," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 312-320.
    16. Kaya, Gulsum Kubra & Hocaoglu, Mehmet Fatih, 2020. "Semi-quantitative application to the Functional Resonance Analysis Method for supporting safety management in a complex health-care process," Reliability Engineering and System Safety, Elsevier, vol. 202(C).

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