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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Garaniya, Vikram
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (13/13 displayed)
- 2024Classification of pitting corrosion damage in process facilities using supervised machine learningcitations
- 2022Experimental analysis of pitting corrosion in offshore structurescitations
- 2020Pitting corrosion modelling of X80 steel utilized in offshore petroleum pipelinescitations
- 2018Condition monitoring of subsea pipelines considering stress observation and structural deteriorationcitations
- 2017Modelling the impacts of fire in a typical FLNG processing facility
- 2017Modelling the impacts of fire in a typical FLNG processing facility
- 2017Accelerated pitting corrosion test of 304 stainless steel using ASTM G48; Experimental investigation and concomitant challengescitations
- 2017Integrated probabilistic modelling of pitting and corrosion fatigue damage of subsea pipelines
- 2017Pitting degradation modelling of ocean steel structures using Bayesian networkcitations
- 2016Dynamic risk-based maintenance for offshore processing facilitycitations
- 2016Reliability assessment of offshore asset under pitting corrosion using Bayesian Network
- 2016Reliability assessment of offshore asset under pitting corrosion using Bayesian Network
- 2015Modelling of pitting corrosion in marine and offshore steel structures - A technical reviewcitations
Places of action
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article
Dynamic risk-based maintenance for offshore processing facility
Abstract
Processing facilities in a marine environment may not remain safe and available if they are not well maintained. Dynamic risk-based maintenance (RBM) methodology is a tool for maintenance planning and decision making, used to enhance the safety and availability of the equipment. It also assists in identifying and prioritizing the maintenance of equipment based on the level of risk. This article discusses an advanced methodology for the design of an optimum maintenance program integrating a dynamic risk-based approach with a maintenance optimization technique. In this study, Bayesian Network (BN) is employed to develop a new dynamic RBM methodology that is capable of using accident precursor information in order to revise the risk profile. The use of this methodology is based on its failure prediction capability which optimizes the cost of maintenance. The developed methodology is applied to a case study involving a failure of a separator system in the offshore oil and gas production platform considering marine environments. The result shows it is essential that the valve system in the separator needs to be planned for maintenance once every 25 days; however, the cooler system can be planned for repairs once only biennially. A sensitivity analysis is also conducted to study the criticality of the operating system.