Optimising Maintenance Planning and Integrity in Offshore Facilities Using Machine Learning and Design Science: A Predictive Approach
This research presents an innovative solution to optimise maintenance planning and integrity in offshore facilities, specifically regarding corrosion management. The study introduces a prototype for maintenance planning on offshore oil platforms, developed through the Design Science Research (DSR) m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/23/10902 |
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| author | Marina Polonia Rios Rodrigo Goyannes Gusmão Caiado Yiselis Rodríguez Vignon Eduardo Thadeu Corseuil Paulo Ivson Netto Santos |
| author_facet | Marina Polonia Rios Rodrigo Goyannes Gusmão Caiado Yiselis Rodríguez Vignon Eduardo Thadeu Corseuil Paulo Ivson Netto Santos |
| author_sort | Marina Polonia Rios |
| collection | DOAJ |
| description | This research presents an innovative solution to optimise maintenance planning and integrity in offshore facilities, specifically regarding corrosion management. The study introduces a prototype for maintenance planning on offshore oil platforms, developed through the Design Science Research (DSR) methodology. Using a 3D CAD/CAE model, the prototype integrates machine learning models to predict corrosion progression, essential for effective maintenance strategies. Key components include damage assessment, regulatory compliance, asset criticality, and resource optimisation, collectively enabling precise and efficient anti-corrosion plans. Case studies on oil and gas platforms validate the practical application of this methodology, demonstrating reduced costs, lower risks associated with corrosion, and enhanced planning efficiency. Additionally, the research opens pathways for future advancements, such as integrating IoT technologies for real-time data collection and applying deep learning models to improve predictive accuracy. These potential extensions aim to evolve the system into a more adaptable and powerful tool for industrial maintenance, with applicability beyond offshore to other environments, including onshore facilities. |
| format | Article |
| id | doaj-art-5b6a3a73fa5e417b8b838d07e50b5ad5 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5b6a3a73fa5e417b8b838d07e50b5ad52024-12-13T16:22:08ZengMDPI AGApplied Sciences2076-34172024-11-0114231090210.3390/app142310902Optimising Maintenance Planning and Integrity in Offshore Facilities Using Machine Learning and Design Science: A Predictive ApproachMarina Polonia Rios0Rodrigo Goyannes Gusmão Caiado1Yiselis Rodríguez Vignon2Eduardo Thadeu Corseuil3Paulo Ivson Netto Santos4Tecgraf Institute, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro 22453-900, BrazilTecgraf Institute, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro 22453-900, BrazilTecgraf Institute, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro 22453-900, BrazilTecgraf Institute, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro 22453-900, BrazilTecgraf Institute, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro 22453-900, BrazilThis research presents an innovative solution to optimise maintenance planning and integrity in offshore facilities, specifically regarding corrosion management. The study introduces a prototype for maintenance planning on offshore oil platforms, developed through the Design Science Research (DSR) methodology. Using a 3D CAD/CAE model, the prototype integrates machine learning models to predict corrosion progression, essential for effective maintenance strategies. Key components include damage assessment, regulatory compliance, asset criticality, and resource optimisation, collectively enabling precise and efficient anti-corrosion plans. Case studies on oil and gas platforms validate the practical application of this methodology, demonstrating reduced costs, lower risks associated with corrosion, and enhanced planning efficiency. Additionally, the research opens pathways for future advancements, such as integrating IoT technologies for real-time data collection and applying deep learning models to improve predictive accuracy. These potential extensions aim to evolve the system into a more adaptable and powerful tool for industrial maintenance, with applicability beyond offshore to other environments, including onshore facilities.https://www.mdpi.com/2076-3417/14/23/10902offshore maintenancecorrosion predictionmaintenance optimisationmachine learningmulti-criteria decision makingreliability centred maintenance |
| spellingShingle | Marina Polonia Rios Rodrigo Goyannes Gusmão Caiado Yiselis Rodríguez Vignon Eduardo Thadeu Corseuil Paulo Ivson Netto Santos Optimising Maintenance Planning and Integrity in Offshore Facilities Using Machine Learning and Design Science: A Predictive Approach Applied Sciences offshore maintenance corrosion prediction maintenance optimisation machine learning multi-criteria decision making reliability centred maintenance |
| title | Optimising Maintenance Planning and Integrity in Offshore Facilities Using Machine Learning and Design Science: A Predictive Approach |
| title_full | Optimising Maintenance Planning and Integrity in Offshore Facilities Using Machine Learning and Design Science: A Predictive Approach |
| title_fullStr | Optimising Maintenance Planning and Integrity in Offshore Facilities Using Machine Learning and Design Science: A Predictive Approach |
| title_full_unstemmed | Optimising Maintenance Planning and Integrity in Offshore Facilities Using Machine Learning and Design Science: A Predictive Approach |
| title_short | Optimising Maintenance Planning and Integrity in Offshore Facilities Using Machine Learning and Design Science: A Predictive Approach |
| title_sort | optimising maintenance planning and integrity in offshore facilities using machine learning and design science a predictive approach |
| topic | offshore maintenance corrosion prediction maintenance optimisation machine learning multi-criteria decision making reliability centred maintenance |
| url | https://www.mdpi.com/2076-3417/14/23/10902 |
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