Advanced data-driven fault detection in gas-to-liquid plants
<p dir="ltr">Fault detection is a critical part of process monitoring, where the objective is to flag unexpected operating behavior quickly and accurately. In this paper, a novel extension of the Generalized Likelihood Ratio charts is proposed, denoted as the Maximum Multivariate GLR...
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2025
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| _version_ | 1864513508223221760 |
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| author | Nour Basha (21385547) |
| author2 | Radhia Fezai (16869888) Byanne Malluhi (22963447) Khaled Dhibi (16891524) Gasim Ibrahim (17032299) Hanif A. Choudhury (1868542) Mohamed S. Challiwala (14152839) Hazem Nounou (16869900) Nimir Elbashir (5244551) Mohamed Nounou (3489386) |
| author2_role | author author author author author author author author author |
| author_facet | Nour Basha (21385547) Radhia Fezai (16869888) Byanne Malluhi (22963447) Khaled Dhibi (16891524) Gasim Ibrahim (17032299) Hanif A. Choudhury (1868542) Mohamed S. Challiwala (14152839) Hazem Nounou (16869900) Nimir Elbashir (5244551) Mohamed Nounou (3489386) |
| author_role | author |
| dc.creator.none.fl_str_mv | Nour Basha (21385547) Radhia Fezai (16869888) Byanne Malluhi (22963447) Khaled Dhibi (16891524) Gasim Ibrahim (17032299) Hanif A. Choudhury (1868542) Mohamed S. Challiwala (14152839) Hazem Nounou (16869900) Nimir Elbashir (5244551) Mohamed Nounou (3489386) |
| dc.date.none.fl_str_mv | 2025-03-19T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compchemeng.2025.109098 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Advanced_data-driven_fault_detection_in_gas-to-liquid_plants/31017736 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Chemical engineering Manufacturing engineering Fault detection Generalized Likelihood Ratio Gas-to-liquid process FischEr–Tropsch Synthesis neural network Kernel Principal Component Analysis |
| dc.title.none.fl_str_mv | Advanced data-driven fault detection in gas-to-liquid plants |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Fault detection is a critical part of process monitoring, where the objective is to flag unexpected operating behavior quickly and accurately. In this paper, a novel extension of the Generalized Likelihood Ratio charts is proposed, denoted as the Maximum Multivariate GLR charts. Linear and nonlinear data-driven models, namely principal component analysis and its kernel extension and <u>neural networks</u>, are combined with different statistical charts towards the detection of multiple fault types in three distinct case studies: synthetic, Tennessee Eastman process, and Gas-to-Liquid process. The results show that the MMGLR charts have a better detection accuracy than conventional charts, and that <u>neural networks</u> are more robust modeling techniques than PCA and KPCA for the sake of fault detection.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Computers & Chemical Engineering<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compchemeng.2025.109098" target="_blank">https://dx.doi.org/10.1016/j.compchemeng.2025.109098</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_124e3dd9b02adaea6b18e9b7b98e3cbe |
| identifier_str_mv | 10.1016/j.compchemeng.2025.109098 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/31017736 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Advanced data-driven fault detection in gas-to-liquid plantsNour Basha (21385547)Radhia Fezai (16869888)Byanne Malluhi (22963447)Khaled Dhibi (16891524)Gasim Ibrahim (17032299)Hanif A. Choudhury (1868542)Mohamed S. Challiwala (14152839)Hazem Nounou (16869900)Nimir Elbashir (5244551)Mohamed Nounou (3489386)EngineeringChemical engineeringManufacturing engineeringFault detectionGeneralized Likelihood RatioGas-to-liquid processFischEr–Tropsch Synthesisneural networkKernel Principal Component Analysis<p dir="ltr">Fault detection is a critical part of process monitoring, where the objective is to flag unexpected operating behavior quickly and accurately. In this paper, a novel extension of the Generalized Likelihood Ratio charts is proposed, denoted as the Maximum Multivariate GLR charts. Linear and nonlinear data-driven models, namely principal component analysis and its kernel extension and <u>neural networks</u>, are combined with different statistical charts towards the detection of multiple fault types in three distinct case studies: synthetic, Tennessee Eastman process, and Gas-to-Liquid process. The results show that the MMGLR charts have a better detection accuracy than conventional charts, and that <u>neural networks</u> are more robust modeling techniques than PCA and KPCA for the sake of fault detection.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Computers & Chemical Engineering<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compchemeng.2025.109098" target="_blank">https://dx.doi.org/10.1016/j.compchemeng.2025.109098</a></p>2025-03-19T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compchemeng.2025.109098https://figshare.com/articles/journal_contribution/Advanced_data-driven_fault_detection_in_gas-to-liquid_plants/31017736CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/310177362025-03-19T12:00:00Z |
| spellingShingle | Advanced data-driven fault detection in gas-to-liquid plants Nour Basha (21385547) Engineering Chemical engineering Manufacturing engineering Fault detection Generalized Likelihood Ratio Gas-to-liquid process FischEr–Tropsch Synthesis neural network Kernel Principal Component Analysis |
| status_str | publishedVersion |
| title | Advanced data-driven fault detection in gas-to-liquid plants |
| title_full | Advanced data-driven fault detection in gas-to-liquid plants |
| title_fullStr | Advanced data-driven fault detection in gas-to-liquid plants |
| title_full_unstemmed | Advanced data-driven fault detection in gas-to-liquid plants |
| title_short | Advanced data-driven fault detection in gas-to-liquid plants |
| title_sort | Advanced data-driven fault detection in gas-to-liquid plants |
| topic | Engineering Chemical engineering Manufacturing engineering Fault detection Generalized Likelihood Ratio Gas-to-liquid process FischEr–Tropsch Synthesis neural network Kernel Principal Component Analysis |