Abstract
Reliability-Centered Maintenance (RCM) is widely used for enterprise asset management in the Oil and Gas sector. The traditional RCM process encompasses the development of equipment hierarchy, the determination of the functional failure of assets, identification of the critical failure modes and the development of suitable maintenance strategies for these failure modes through RCM logic tree analysis. However, the current industry standards and conditions call for greater strides of production availability and reliability of the assets. The aim of the paper is to deliver RCM oriented cost optimization framework for the assets through the implementation of probabilistic and statistical analysis. Probabilistic analysis of the most critical components predicts the failure over time. The mean time to failure, mean time between failure and mean time to repair parameters are determined from the failure history. The parameters of the probability distribution, for instance, the shape and scale parameters of Weibull distribution determine the failure pattern of the failure mode over time. The cost optimization modules allow the analyst to determine the optimum intervals for maintenance or replacement of the maintainable items. The integrated RCM methodology, therefore, provides quantitative and cost-effective solutions for asset maintenance and management. A case study of an equipment in the Oil and Gas sector will be presented to illustrate the optimization framework.
Original language | English |
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Article number | 012007 |
Number of pages | 7 |
Journal | Journal of Physics: Conference Series |
Volume | 1730 |
Issue number | 1 |
DOIs | |
Publication status | Published - 3 Feb 2021 |
Event | 9th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2020 - Tinos Island, Virtual, Greece Duration: 7 Sept 2020 → 10 Sept 2020 |
Bibliographical note
Funding Information:This research project has been funded and supported by Innovate UK and Optimal as part Knowledge Transfer Partnerships Scheme.