Bayesian belief networks for fault detection and diagnostics of a three-phase separator
A three-phase separator (TPS) is one of the key components of offshore oil processing facili-ties. Oil is separated from gas, water and solid impurities by the TPS before it can be further processed. Fail-ures of the TPS can lead to unplanned shutdowns and reduction of the efficiency of the whole oi...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
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2016
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| Online Access: | https://eprints.nottingham.ac.uk/34552/ |
| _version_ | 1848794880727318528 |
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| author | Vileiniskis, Marius Remenyte-Prescott, Rasa Rama, Dovile Andrews, John D. |
| author_facet | Vileiniskis, Marius Remenyte-Prescott, Rasa Rama, Dovile Andrews, John D. |
| author_sort | Vileiniskis, Marius |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | A three-phase separator (TPS) is one of the key components of offshore oil processing facili-ties. Oil is separated from gas, water and solid impurities by the TPS before it can be further processed. Fail-ures of the TPS can lead to unplanned shutdowns and reduction of the efficiency of the whole oil processing facility as well as posing hazards to safety of personnel. A novel fault detection and diagnostic (FDD) meth-odology for the TPS is proposed in this paper. The core of the methodology is based on Bayesian Belief Net-works (BBN). A BBN model is built to replicate the operation of the TPS: when the system is fault free or operating with single or multiple failed components. Results of the capabilities of the BBN model to detect and diagnose single and multiple faults of the TPS components are reported in this paper. |
| first_indexed | 2025-11-14T19:23:14Z |
| format | Conference or Workshop Item |
| id | nottingham-34552 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:23:14Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-345522020-05-04T17:56:52Z https://eprints.nottingham.ac.uk/34552/ Bayesian belief networks for fault detection and diagnostics of a three-phase separator Vileiniskis, Marius Remenyte-Prescott, Rasa Rama, Dovile Andrews, John D. A three-phase separator (TPS) is one of the key components of offshore oil processing facili-ties. Oil is separated from gas, water and solid impurities by the TPS before it can be further processed. Fail-ures of the TPS can lead to unplanned shutdowns and reduction of the efficiency of the whole oil processing facility as well as posing hazards to safety of personnel. A novel fault detection and diagnostic (FDD) meth-odology for the TPS is proposed in this paper. The core of the methodology is based on Bayesian Belief Net-works (BBN). A BBN model is built to replicate the operation of the TPS: when the system is fault free or operating with single or multiple failed components. Results of the capabilities of the BBN model to detect and diagnose single and multiple faults of the TPS components are reported in this paper. 2016-06-13 Conference or Workshop Item PeerReviewed Vileiniskis, Marius, Remenyte-Prescott, Rasa, Rama, Dovile and Andrews, John D. (2016) Bayesian belief networks for fault detection and diagnostics of a three-phase separator. In: ESREL 2016, 25-29 Sept 2016, Glasgow, UK. (In Press) |
| spellingShingle | Vileiniskis, Marius Remenyte-Prescott, Rasa Rama, Dovile Andrews, John D. Bayesian belief networks for fault detection and diagnostics of a three-phase separator |
| title | Bayesian belief networks for fault detection and diagnostics of a three-phase separator |
| title_full | Bayesian belief networks for fault detection and diagnostics of a three-phase separator |
| title_fullStr | Bayesian belief networks for fault detection and diagnostics of a three-phase separator |
| title_full_unstemmed | Bayesian belief networks for fault detection and diagnostics of a three-phase separator |
| title_short | Bayesian belief networks for fault detection and diagnostics of a three-phase separator |
| title_sort | bayesian belief networks for fault detection and diagnostics of a three-phase separator |
| url | https://eprints.nottingham.ac.uk/34552/ |