Version 1
: Received: 8 January 2020 / Approved: 9 January 2020 / Online: 9 January 2020 (13:22:43 CET)
How to cite:
Petrillo, A.; Ranieri, L.; Petrillo, L.; De Felice, F. The Functional Resonance Analysis Approach to Assess Performance Variability During Emergency Conditions. Preprints2020, 2020010088. https://doi.org/10.20944/preprints202001.0088.v1
Petrillo, A.; Ranieri, L.; Petrillo, L.; De Felice, F. The Functional Resonance Analysis Approach to Assess Performance Variability During Emergency Conditions. Preprints 2020, 2020010088. https://doi.org/10.20944/preprints202001.0088.v1
Petrillo, A.; Ranieri, L.; Petrillo, L.; De Felice, F. The Functional Resonance Analysis Approach to Assess Performance Variability During Emergency Conditions. Preprints2020, 2020010088. https://doi.org/10.20944/preprints202001.0088.v1
APA Style
Petrillo, A., Ranieri, L., Petrillo, L., & De Felice, F. (2020). The Functional Resonance Analysis Approach to Assess Performance Variability During Emergency Conditions. Preprints. https://doi.org/10.20944/preprints202001.0088.v1
Chicago/Turabian Style
Petrillo, A., Laura Petrillo and Fabio De Felice. 2020 "The Functional Resonance Analysis Approach to Assess Performance Variability During Emergency Conditions" Preprints. https://doi.org/10.20944/preprints202001.0088.v1
Abstract
Technological innovation has led to the development of increasingly efficient and complex industrial plants. To manage this complexity, it is necessary to define an integrated vision of the socio-technological system that includes: technological, human and organizational component. Petrochemicals can be considered one of the most complex socio-technical systems that deserve special attention to high risk management, especially during the emergency conditions. Traditional safety management models only consider static systems, while new resilience engineering models evaluate the performance variability developed between different actions. One of the recent development methods is the Functional Resonance Analysis Method (FRAM) that identifies the pairs between the functions. FRAM unfortunately is a qualitative model, this research integrates this model with the Performance Shaping Factors (PSFs) and with the Bayesian approach to identify the performance variability of the system. The analysis aims to develop a system that improves safety analysis. The proposed model is applied in a case study of an emergency in a petrochemical company.
Keywords
human reliability analysis; safety; FRAM; resilience engineering; performance variability; emergency
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.