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An empirical classification-based framework for the safety criticality assessment of energy production systems, in presence of inconsistent data

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  • Wang, Tai-Ran
  • Mousseau, Vincent
  • Pedroni, Nicola
  • Zio, Enrico

Abstract

The technical problem addressed in the present paper is the assessment of the safety criticality of energy production systems. An empirical classification model is developed, based on the Majority Rule Sorting method, to evaluate the class of criticality of the plant/system of interest, with respect to safety. The model is built on the basis of a (limited-size) set of data representing the characteristics of a number of plants and their corresponding criticality classes, as assigned by experts.

Suggested Citation

  • Wang, Tai-Ran & Mousseau, Vincent & Pedroni, Nicola & Zio, Enrico, 2017. "An empirical classification-based framework for the safety criticality assessment of energy production systems, in presence of inconsistent data," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 139-151.
    • Handle: RePEc:eee:reensy:v:157:y:2017:i:c:p:139-151
    DOI: 10.1016/j.ress.2016.08.021
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    Cited by:

    1. Marco Cinelli & Matteo Spada & Miłosz Kadziński & Grzegorz Miebs & Peter Burgherr, 2019. "Advancing Hazard Assessment of Energy Accidents in the Natural Gas Sector with Rough Set Theory and Decision Rules," Energies, MDPI, vol. 12(21), pages 1-17, November.
    2. Ahmed, Umair & Carpitella, Silvia & Certa, Antonella, 2021. "An integrated methodological approach for optimising complex systems subjected to predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

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