GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults
<p dir="ltr">Electrical and mechanical equipment with rotating parts often face the challenge of early breakdown due to defects in the gears or rolling bearings. Automated industrial systems can be significantly impeded by this type of fault in revolving components because of manual...
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2024
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| _version_ | 1864513541658116096 |
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| author | Proma Dutta (19459567) |
| author2 | Kanchon Kanti Podder (22045904) Md. Shaheenur Islam Sumon (22045907) Muhammad E. H. Chowdhury (14150526) Amith Khandakar (14151981) Nasser Al-Emadi (16864200) Moajjem Hossain Chowdhury (21842429) M. Murugappan (18842221) Mohamed Arselene Ayari (17873878) Sakib Mahmud (15302404) S. M. Muyeen (14778337) |
| author2_role | author author author author author author author author author author |
| author_facet | Proma Dutta (19459567) Kanchon Kanti Podder (22045904) Md. Shaheenur Islam Sumon (22045907) Muhammad E. H. Chowdhury (14150526) Amith Khandakar (14151981) Nasser Al-Emadi (16864200) Moajjem Hossain Chowdhury (21842429) M. Murugappan (18842221) Mohamed Arselene Ayari (17873878) Sakib Mahmud (15302404) S. M. Muyeen (14778337) |
| author_role | author |
| dc.creator.none.fl_str_mv | Proma Dutta (19459567) Kanchon Kanti Podder (22045904) Md. Shaheenur Islam Sumon (22045907) Muhammad E. H. Chowdhury (14150526) Amith Khandakar (14151981) Nasser Al-Emadi (16864200) Moajjem Hossain Chowdhury (21842429) M. Murugappan (18842221) Mohamed Arselene Ayari (17873878) Sakib Mahmud (15302404) S. M. Muyeen (14778337) |
| dc.date.none.fl_str_mv | 2024-12-20T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3412274 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/GearFaultNet_Novel_Network_for_Automatic_and_Early_Detection_of_Gearbox_Faults/29899097 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Mechanical engineering Information and computing sciences Artificial intelligence Machine learning GearFaultNet fault detection gearbox 1D-CNN deep learning Gears Fault detection Wind turbines Monitoring Vibrations Fault diagnosis Deep learning |
| dc.title.none.fl_str_mv | GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Electrical and mechanical equipment with rotating parts often face the challenge of early breakdown due to defects in the gears or rolling bearings. Automated industrial systems can be significantly impeded by this type of fault in revolving components because of manual fault detection and the additional time required for repairing and replacing them. This research presents GearFaultNet, a novel, lightweight 1D Convolutional Neural Network (CNN)-based network, designed to detect gearbox faults. GearFaultNet can be an effective measure for real-time detection of sudden shutdowns and can alleviate downtime and system losses in the industrial aspect. The proposed framework involves the integration of four-channel vibration data from different loading conditions, which are preprocessed in the temporal domain and fed to GearFaultNet to classify the gearbox’s condition as either Healthy or Broken. The developed lightweight deep learning network has achieved higher accuracy than those proposed in existing literature. The overall accuracy achieved by this framework is 94.04%. This shallow network can also be applied to estimate other mechanical faults in different machinery.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3412274" target="_blank">https://dx.doi.org/10.1109/access.2024.3412274</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a6848f3200f23a9cef7ab832146005f2 |
| identifier_str_mv | 10.1109/access.2024.3412274 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29899097 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox FaultsProma Dutta (19459567)Kanchon Kanti Podder (22045904)Md. Shaheenur Islam Sumon (22045907)Muhammad E. H. Chowdhury (14150526)Amith Khandakar (14151981)Nasser Al-Emadi (16864200)Moajjem Hossain Chowdhury (21842429)M. Murugappan (18842221)Mohamed Arselene Ayari (17873878)Sakib Mahmud (15302404)S. M. Muyeen (14778337)EngineeringMechanical engineeringInformation and computing sciencesArtificial intelligenceMachine learningGearFaultNetfault detectiongearbox1D-CNNdeep learningGearsFault detectionWind turbinesMonitoringVibrationsFault diagnosisDeep learning<p dir="ltr">Electrical and mechanical equipment with rotating parts often face the challenge of early breakdown due to defects in the gears or rolling bearings. Automated industrial systems can be significantly impeded by this type of fault in revolving components because of manual fault detection and the additional time required for repairing and replacing them. This research presents GearFaultNet, a novel, lightweight 1D Convolutional Neural Network (CNN)-based network, designed to detect gearbox faults. GearFaultNet can be an effective measure for real-time detection of sudden shutdowns and can alleviate downtime and system losses in the industrial aspect. The proposed framework involves the integration of four-channel vibration data from different loading conditions, which are preprocessed in the temporal domain and fed to GearFaultNet to classify the gearbox’s condition as either Healthy or Broken. The developed lightweight deep learning network has achieved higher accuracy than those proposed in existing literature. The overall accuracy achieved by this framework is 94.04%. This shallow network can also be applied to estimate other mechanical faults in different machinery.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3412274" target="_blank">https://dx.doi.org/10.1109/access.2024.3412274</a></p>2024-12-20T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3412274https://figshare.com/articles/journal_contribution/GearFaultNet_Novel_Network_for_Automatic_and_Early_Detection_of_Gearbox_Faults/29899097CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/298990972024-12-20T15:00:00Z |
| spellingShingle | GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults Proma Dutta (19459567) Engineering Mechanical engineering Information and computing sciences Artificial intelligence Machine learning GearFaultNet fault detection gearbox 1D-CNN deep learning Gears Fault detection Wind turbines Monitoring Vibrations Fault diagnosis Deep learning |
| status_str | publishedVersion |
| title | GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults |
| title_full | GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults |
| title_fullStr | GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults |
| title_full_unstemmed | GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults |
| title_short | GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults |
| title_sort | GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults |
| topic | Engineering Mechanical engineering Information and computing sciences Artificial intelligence Machine learning GearFaultNet fault detection gearbox 1D-CNN deep learning Gears Fault detection Wind turbines Monitoring Vibrations Fault diagnosis Deep learning |