Zero-shot motor health monitoring by blind domain transition
<p>Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (health...
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2024
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| _version_ | 1864513528302403584 |
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| author | Serkan Kiranyaz (3762058) |
| author2 | Ozer Can Devecioglu (17910575) Amir Alhams (17910578) Sadok Sassi (17823461) Turker Ince (14150610) Osama Abdeljaber (14246798) Onur Avci (14246801) Moncef Gabbouj (2276533) |
| author2_role | author author author author author author author |
| author_facet | Serkan Kiranyaz (3762058) Ozer Can Devecioglu (17910575) Amir Alhams (17910578) Sadok Sassi (17823461) Turker Ince (14150610) Osama Abdeljaber (14246798) Onur Avci (14246801) Moncef Gabbouj (2276533) |
| author_role | author |
| dc.creator.none.fl_str_mv | Serkan Kiranyaz (3762058) Ozer Can Devecioglu (17910575) Amir Alhams (17910578) Sadok Sassi (17823461) Turker Ince (14150610) Osama Abdeljaber (14246798) Onur Avci (14246801) Moncef Gabbouj (2276533) |
| dc.date.none.fl_str_mv | 2024-03-15T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.ymssp.2024.111147 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Zero-shot_motor_health_monitoring_by_blind_domain_transition/25151669 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Aerospace engineering Control engineering, mechatronics and robotics Mechanical engineering Operational Neural Networks Bearing fault detection 1D operational GANs Machine Health Monitoring Blind domain transition |
| dc.title.none.fl_str_mv | Zero-shot motor health monitoring by blind domain transition |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the recent deep learning (DL) methodologies trained on the labeled data from the same machine, the classification accuracy significantly deteriorates when one or few conditions are altered, e.g., a different speed or load, or for different fault types/severities with sensors placed in different locations. Furthermore, their performance suffers significantly or may entirely fail when they are tested on another machine with entirely different healthy and faulty signal patterns. To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. To accomplish this objective, a 1D Operational Generative Adversarial Network (Op-GAN) first characterizes the transition between normal and fault vibration signals of (a) source machine(s) under various conditions, sensor parameters, and fault types. Then for a target machine, the potential faulty signals can be generated, and over its actual healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN fault detector can then be trained to detect the real faulty condition in real time whenever it occurs. To validate the proposed approach, a new benchmark dataset is created using two different motors working under different conditions and sensor locations. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.</p><h2>Other Information</h2> <p> Published in: Mechanical Systems and Signal Processing<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.ymssp.2024.111147" target="_blank">https://dx.doi.org/10.1016/j.ymssp.2024.111147</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1462695ceb96bbc206671742d44e0ba1 |
| identifier_str_mv | 10.1016/j.ymssp.2024.111147 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25151669 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Zero-shot motor health monitoring by blind domain transitionSerkan Kiranyaz (3762058)Ozer Can Devecioglu (17910575)Amir Alhams (17910578)Sadok Sassi (17823461)Turker Ince (14150610)Osama Abdeljaber (14246798)Onur Avci (14246801)Moncef Gabbouj (2276533)EngineeringAerospace engineeringControl engineering, mechatronics and roboticsMechanical engineeringOperational Neural NetworksBearing fault detection1D operational GANsMachine Health MonitoringBlind domain transition<p>Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the recent deep learning (DL) methodologies trained on the labeled data from the same machine, the classification accuracy significantly deteriorates when one or few conditions are altered, e.g., a different speed or load, or for different fault types/severities with sensors placed in different locations. Furthermore, their performance suffers significantly or may entirely fail when they are tested on another machine with entirely different healthy and faulty signal patterns. To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. To accomplish this objective, a 1D Operational Generative Adversarial Network (Op-GAN) first characterizes the transition between normal and fault vibration signals of (a) source machine(s) under various conditions, sensor parameters, and fault types. Then for a target machine, the potential faulty signals can be generated, and over its actual healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN fault detector can then be trained to detect the real faulty condition in real time whenever it occurs. To validate the proposed approach, a new benchmark dataset is created using two different motors working under different conditions and sensor locations. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.</p><h2>Other Information</h2> <p> Published in: Mechanical Systems and Signal Processing<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.ymssp.2024.111147" target="_blank">https://dx.doi.org/10.1016/j.ymssp.2024.111147</a></p>2024-03-15T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.ymssp.2024.111147https://figshare.com/articles/journal_contribution/Zero-shot_motor_health_monitoring_by_blind_domain_transition/25151669CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/251516692024-03-15T03:00:00Z |
| spellingShingle | Zero-shot motor health monitoring by blind domain transition Serkan Kiranyaz (3762058) Engineering Aerospace engineering Control engineering, mechatronics and robotics Mechanical engineering Operational Neural Networks Bearing fault detection 1D operational GANs Machine Health Monitoring Blind domain transition |
| status_str | publishedVersion |
| title | Zero-shot motor health monitoring by blind domain transition |
| title_full | Zero-shot motor health monitoring by blind domain transition |
| title_fullStr | Zero-shot motor health monitoring by blind domain transition |
| title_full_unstemmed | Zero-shot motor health monitoring by blind domain transition |
| title_short | Zero-shot motor health monitoring by blind domain transition |
| title_sort | Zero-shot motor health monitoring by blind domain transition |
| topic | Engineering Aerospace engineering Control engineering, mechatronics and robotics Mechanical engineering Operational Neural Networks Bearing fault detection 1D operational GANs Machine Health Monitoring Blind domain transition |