Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operator
<p dir="ltr">The <u>gas turbine engine</u> is a predominant <u>prime mover</u> in the transport and energy sectors, and ensuring its reliable operation holds paramount significance. While intelligent fault diagnosis (FD) approaches have seen successful advance...
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| مؤلفون آخرون: | , , |
| منشور في: |
2024
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| _version_ | 1864513513462956032 |
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| author | Fatemeh Negar Irani (22302835) |
| author2 | Mohammadjavad Soleimani (22302838) Meysam Yadegar (16410089) Nader Meskin (14147796) |
| author2_role | author author author |
| author_facet | Fatemeh Negar Irani (22302835) Mohammadjavad Soleimani (22302838) Meysam Yadegar (16410089) Nader Meskin (14147796) |
| author_role | author |
| dc.creator.none.fl_str_mv | Fatemeh Negar Irani (22302835) Mohammadjavad Soleimani (22302838) Meysam Yadegar (16410089) Nader Meskin (14147796) |
| dc.date.none.fl_str_mv | 2024-04-20T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.apenergy.2024.123256 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Deep_transfer_learning_strategy_in_intelligent_fault_diagnosis_of_gas_turbines_based_on_the_Koopman_operator/30197188 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Aerospace engineering Mechanical engineering Information and computing sciences Artificial intelligence Gas turbine Fault diagnosis Data-driven Transfer learning Koopman operator Geometric |
| dc.title.none.fl_str_mv | Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operator |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The <u>gas turbine engine</u> is a predominant <u>prime mover</u> in the transport and energy sectors, and ensuring its reliable operation holds paramount significance. While intelligent fault diagnosis (FD) approaches have seen successful advancements within the <u>gas turbine</u> FD landscape, many existing methods operate under the assumption of identical health states during both data collection and the FD process. Moreover, most previous studies have overlooked the diagnosis of both sensors and <u>actuators</u>. Another critical challenge lies in isolating simultaneous and multiple faults and providing compromising FD performance, especially in the face of continued system <u>performance</u> <u>degradation</u>. Aiming at these problems, this study develops a novel unsupervised data-driven FD strategy based on leveraging the potential of <u>transfer learning</u> and the Koopman operator. A <u>deep neural network</u>-based transfer learning framework is proposed for realizing a precise adaptive linear model called the deep transfer linear (DTL) model enabling reliable prediction of the system’s behavior in various situations and designing structured fault residuals. To this end, a deep neural network framework is used to obtain a precise Koopman model realization using the richly collected data in the source domain. Subsequently, the realized model is fine-tuned for the target domains associated with the degraded system, mitigating the adverse effects of domain shift and addressing the rich data scarcity problem in the target domain. In addition, the dedicated and generalized <u>residual sets</u> are designed and generated employing the geometric approach for fault isolation and a decision-making analysis is developed to diagnose simultaneous faults. The reliability of the proposed strategy is demonstrated through various experiments in the presence of noise and performance degradation, and a comparative performance analysis is conducted between the proposed strategy and another data-driven method showcasing the superiority of the proposed approach.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Energy<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.apenergy.2024.123256" target="_blank">https://dx.doi.org/10.1016/j.apenergy.2024.123256</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_e07beb79fd641c4a882625ac52ecb061 |
| identifier_str_mv | 10.1016/j.apenergy.2024.123256 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30197188 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operatorFatemeh Negar Irani (22302835)Mohammadjavad Soleimani (22302838)Meysam Yadegar (16410089)Nader Meskin (14147796)EngineeringAerospace engineeringMechanical engineeringInformation and computing sciencesArtificial intelligenceGas turbineFault diagnosisData-drivenTransfer learningKoopman operatorGeometric<p dir="ltr">The <u>gas turbine engine</u> is a predominant <u>prime mover</u> in the transport and energy sectors, and ensuring its reliable operation holds paramount significance. While intelligent fault diagnosis (FD) approaches have seen successful advancements within the <u>gas turbine</u> FD landscape, many existing methods operate under the assumption of identical health states during both data collection and the FD process. Moreover, most previous studies have overlooked the diagnosis of both sensors and <u>actuators</u>. Another critical challenge lies in isolating simultaneous and multiple faults and providing compromising FD performance, especially in the face of continued system <u>performance</u> <u>degradation</u>. Aiming at these problems, this study develops a novel unsupervised data-driven FD strategy based on leveraging the potential of <u>transfer learning</u> and the Koopman operator. A <u>deep neural network</u>-based transfer learning framework is proposed for realizing a precise adaptive linear model called the deep transfer linear (DTL) model enabling reliable prediction of the system’s behavior in various situations and designing structured fault residuals. To this end, a deep neural network framework is used to obtain a precise Koopman model realization using the richly collected data in the source domain. Subsequently, the realized model is fine-tuned for the target domains associated with the degraded system, mitigating the adverse effects of domain shift and addressing the rich data scarcity problem in the target domain. In addition, the dedicated and generalized <u>residual sets</u> are designed and generated employing the geometric approach for fault isolation and a decision-making analysis is developed to diagnose simultaneous faults. The reliability of the proposed strategy is demonstrated through various experiments in the presence of noise and performance degradation, and a comparative performance analysis is conducted between the proposed strategy and another data-driven method showcasing the superiority of the proposed approach.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Energy<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.apenergy.2024.123256" target="_blank">https://dx.doi.org/10.1016/j.apenergy.2024.123256</a></p>2024-04-20T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.apenergy.2024.123256https://figshare.com/articles/journal_contribution/Deep_transfer_learning_strategy_in_intelligent_fault_diagnosis_of_gas_turbines_based_on_the_Koopman_operator/30197188CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/301971882024-04-20T12:00:00Z |
| spellingShingle | Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operator Fatemeh Negar Irani (22302835) Engineering Aerospace engineering Mechanical engineering Information and computing sciences Artificial intelligence Gas turbine Fault diagnosis Data-driven Transfer learning Koopman operator Geometric |
| status_str | publishedVersion |
| title | Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operator |
| title_full | Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operator |
| title_fullStr | Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operator |
| title_full_unstemmed | Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operator |
| title_short | Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operator |
| title_sort | Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operator |
| topic | Engineering Aerospace engineering Mechanical engineering Information and computing sciences Artificial intelligence Gas turbine Fault diagnosis Data-driven Transfer learning Koopman operator Geometric |