A data-driven approach for fault diagnosis in multi-zone HVAC systems: Deep neural bilinear Koopman parity
<p dir="ltr">Sensor faults in heating, ventilation, and air conditioning (HVAC) systems are inevitable and result in significant energy waste. This paper presents an innovative data-driven approach for sensor fault detection and isolation in multi-zone HVAC systems. The proposed solu...
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| مؤلفون آخرون: | , , |
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2023
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| _version_ | 1864513558827499520 |
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| author | Fatemeh Negar Irani (16410087) |
| author2 | Mohammadhosein Bakhtiaridoust (16410088) Meysam Yadegar (16410089) Nader Meskin (14147796) |
| author2_role | author author author |
| author_facet | Fatemeh Negar Irani (16410087) Mohammadhosein Bakhtiaridoust (16410088) Meysam Yadegar (16410089) Nader Meskin (14147796) |
| author_role | author |
| dc.creator.none.fl_str_mv | Fatemeh Negar Irani (16410087) Mohammadhosein Bakhtiaridoust (16410088) Meysam Yadegar (16410089) Nader Meskin (14147796) |
| dc.date.none.fl_str_mv | 2023-10-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.jobe.2023.107127 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_data-driven_approach_for_fault_diagnosis_in_multi-zone_HVAC_systems_Deep_neural_bilinear_Koopman_parity/23551479 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Civil engineering Electrical engineering Information and computing sciences Machine learning HVAC system AHU Koopman operator Sensor fault detection and isolation Bilinear system Data-driven Parity-space method Deep learning |
| dc.title.none.fl_str_mv | A data-driven approach for fault diagnosis in multi-zone HVAC systems: Deep neural bilinear Koopman parity |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Sensor faults in heating, ventilation, and air conditioning (HVAC) systems are inevitable and result in significant energy waste. This paper presents an innovative data-driven approach for sensor fault detection and isolation in multi-zone HVAC systems. The proposed solution integrates bilinear Koopman model realization, deep learning, and bilinear parity-space. A deep neural network realizes a bilinear model, enabling bilinear parity-space sensor fault detection and isolation. This yields a reliable, accurate, and interpretable data-driven framework. The method requires no prior HVAC dynamics knowledge, relying solely on normal operation data. It diagnoses additive, multiplicative, and complete failure sensor faults while minimizing false alarms, even with severe faults. A four-zone HVAC system is simulated in TRNSYS as a case study to demonstrate the performance and efficacy of the proposed approach. The proposed bilinear deep Koopman model realization is utilized to develop a bilinear model for the four-zone HVAC system. The bilinear model is then used for designing the bilinear parity-space. Further, considering various failure scenarios, the proposed sensor fault detection and isolation framework demonstrates promising diagnosis performance. Finally, a comparison is conducted to showcase the advantages of the proposed method over earlier works based on PCA and neural networks.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Building 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="http://dx.doi.org/10.1016/j.jobe.2023.107127" target="_blank">http://dx.doi.org/10.1016/j.jobe.2023.107127</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_89cff39d8afacdeb0ba6c8be01c87811 |
| identifier_str_mv | 10.1016/j.jobe.2023.107127 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/23551479 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A data-driven approach for fault diagnosis in multi-zone HVAC systems: Deep neural bilinear Koopman parityFatemeh Negar Irani (16410087)Mohammadhosein Bakhtiaridoust (16410088)Meysam Yadegar (16410089)Nader Meskin (14147796)EngineeringCivil engineeringElectrical engineeringInformation and computing sciencesMachine learningHVAC systemAHUKoopman operatorSensor fault detection and isolationBilinear systemData-drivenParity-space methodDeep learning<p dir="ltr">Sensor faults in heating, ventilation, and air conditioning (HVAC) systems are inevitable and result in significant energy waste. This paper presents an innovative data-driven approach for sensor fault detection and isolation in multi-zone HVAC systems. The proposed solution integrates bilinear Koopman model realization, deep learning, and bilinear parity-space. A deep neural network realizes a bilinear model, enabling bilinear parity-space sensor fault detection and isolation. This yields a reliable, accurate, and interpretable data-driven framework. The method requires no prior HVAC dynamics knowledge, relying solely on normal operation data. It diagnoses additive, multiplicative, and complete failure sensor faults while minimizing false alarms, even with severe faults. A four-zone HVAC system is simulated in TRNSYS as a case study to demonstrate the performance and efficacy of the proposed approach. The proposed bilinear deep Koopman model realization is utilized to develop a bilinear model for the four-zone HVAC system. The bilinear model is then used for designing the bilinear parity-space. Further, considering various failure scenarios, the proposed sensor fault detection and isolation framework demonstrates promising diagnosis performance. Finally, a comparison is conducted to showcase the advantages of the proposed method over earlier works based on PCA and neural networks.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Building 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="http://dx.doi.org/10.1016/j.jobe.2023.107127" target="_blank">http://dx.doi.org/10.1016/j.jobe.2023.107127</a></p>2023-10-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.jobe.2023.107127https://figshare.com/articles/journal_contribution/A_data-driven_approach_for_fault_diagnosis_in_multi-zone_HVAC_systems_Deep_neural_bilinear_Koopman_parity/23551479CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/235514792023-10-01T00:00:00Z |
| spellingShingle | A data-driven approach for fault diagnosis in multi-zone HVAC systems: Deep neural bilinear Koopman parity Fatemeh Negar Irani (16410087) Engineering Civil engineering Electrical engineering Information and computing sciences Machine learning HVAC system AHU Koopman operator Sensor fault detection and isolation Bilinear system Data-driven Parity-space method Deep learning |
| status_str | publishedVersion |
| title | A data-driven approach for fault diagnosis in multi-zone HVAC systems: Deep neural bilinear Koopman parity |
| title_full | A data-driven approach for fault diagnosis in multi-zone HVAC systems: Deep neural bilinear Koopman parity |
| title_fullStr | A data-driven approach for fault diagnosis in multi-zone HVAC systems: Deep neural bilinear Koopman parity |
| title_full_unstemmed | A data-driven approach for fault diagnosis in multi-zone HVAC systems: Deep neural bilinear Koopman parity |
| title_short | A data-driven approach for fault diagnosis in multi-zone HVAC systems: Deep neural bilinear Koopman parity |
| title_sort | A data-driven approach for fault diagnosis in multi-zone HVAC systems: Deep neural bilinear Koopman parity |
| topic | Engineering Civil engineering Electrical engineering Information and computing sciences Machine learning HVAC system AHU Koopman operator Sensor fault detection and isolation Bilinear system Data-driven Parity-space method Deep learning |