On risk-based maintenance: A comprehensive review of three approaches to track the impact of consequence modelling for predicting maintenance actions

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Journal Title
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Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2021-09
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Mcode
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Language
en
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Series
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, Volume 72
Abstract
Since gas plants are progressively increasing near urban areas, a comprehensive tool to plan maintenance and reduce the risk arising from their operations is required. To this end, a comparison of three Risk-Based Maintenance methodologies able to point out maintenance priorities for the most critical components, is presented in this paper. Moreover, while the literature is mostly focused on probabilistic analysis, a particular attention is directed towards consequence analysis throughout this study. The first developed technique is characterized by a Hierarchical Bayesian Network to perform the occurrence analysis and a Failure Modes, Effects and Criticality Analysis to assess the magnitude of the adverse outcomes. The second approach is a Quantitative Risk Analysis carried out via a software named Safeti. Finally, another software called Synergi Plant is adopted for the third methodology, which provides a Risk-Based Inspection plan, through a semiquantitative risk analysis. The proposed study can assist asset manager in adopting the most appropriate methodology to their context, while highlighting priority components. To demonstrate the applicability of the approaches and compare their rankings, a Natural Gas Regulating and Measuring Station is considered as case study. The results showed that the most suited method strongly depends on the available data.
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Keywords
Hierarchical bayesian approach, Natural gas distribution network, Quantitative risk analysis, Risk-based maintenance
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Citation
Leoni, L, De Carlo, F, Technology, N U O S, Sgarbossa, F & BahooToroody, A 2021, ' On risk-based maintenance : A comprehensive review of three approaches to track the impact of consequence modelling for predicting maintenance actions ', JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, vol. 72, 104555 . https://doi.org/10.1016/j.jlp.2021.104555