Stacking-based ensemble learning for remaining useful life estimation
<p dir="ltr">Excessive and untimely maintenance prompts economic losses and unnecessary workload. Therefore, predictive maintenance models are developed to estimate the right time for maintenance. In this study, predictive models that estimate the remaining useful life of turbofan en...
محفوظ في:
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| مؤلفون آخرون: | , , |
| منشور في: |
2023
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إضافة وسم
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| _version_ | 1864513530947960832 |
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| author | Begum Ay Ture (17773170) |
| author2 | Akhan Akbulut (17380285) Abdul Halim Zaim (17380294) Cagatay Catal (6897842) |
| author2_role | author author author |
| author_facet | Begum Ay Ture (17773170) Akhan Akbulut (17380285) Abdul Halim Zaim (17380294) Cagatay Catal (6897842) |
| author_role | author |
| dc.creator.none.fl_str_mv | Begum Ay Ture (17773170) Akhan Akbulut (17380285) Abdul Halim Zaim (17380294) Cagatay Catal (6897842) |
| dc.date.none.fl_str_mv | 2023-05-21T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00500-023-08322-6 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Stacking-based_ensemble_learning_for_remaining_useful_life_estimation/24980811 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Aerospace engineering Information and computing sciences Data management and data science Machine learning Remaining useful life Ensemble learning Deep learning Stacking ensemble learning |
| dc.title.none.fl_str_mv | Stacking-based ensemble learning for remaining useful life estimation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Excessive and untimely maintenance prompts economic losses and unnecessary workload. Therefore, predictive maintenance models are developed to estimate the right time for maintenance. In this study, predictive models that estimate the remaining useful life of turbofan engines have been developed using deep learning algorithms on NASA’s turbofan engine degradation simulation dataset. Before equipment failure, the proposed model presents an estimated timeline for maintenance. The experimental studies demonstrated that the stacking ensemble learning and the convolutional neural network (CNN) methods are superior to the other investigated methods. While the convolution neural network (CNN) method was superior to the other investigated methods with an accuracy of 93.93%, the stacking ensemble learning method provided the best result with an accuracy of 95.72%.</p><h2>Other Information</h2><p dir="ltr">Published in: Soft Computing<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00500-023-08322-6" target="_blank">https://dx.doi.org/10.1007/s00500-023-08322-6</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_09f258c34a38b7f6d80e7c3354d65e6d |
| identifier_str_mv | 10.1007/s00500-023-08322-6 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24980811 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Stacking-based ensemble learning for remaining useful life estimationBegum Ay Ture (17773170)Akhan Akbulut (17380285)Abdul Halim Zaim (17380294)Cagatay Catal (6897842)EngineeringAerospace engineeringInformation and computing sciencesData management and data scienceMachine learningRemaining useful lifeEnsemble learningDeep learningStacking ensemble learning<p dir="ltr">Excessive and untimely maintenance prompts economic losses and unnecessary workload. Therefore, predictive maintenance models are developed to estimate the right time for maintenance. In this study, predictive models that estimate the remaining useful life of turbofan engines have been developed using deep learning algorithms on NASA’s turbofan engine degradation simulation dataset. Before equipment failure, the proposed model presents an estimated timeline for maintenance. The experimental studies demonstrated that the stacking ensemble learning and the convolutional neural network (CNN) methods are superior to the other investigated methods. While the convolution neural network (CNN) method was superior to the other investigated methods with an accuracy of 93.93%, the stacking ensemble learning method provided the best result with an accuracy of 95.72%.</p><h2>Other Information</h2><p dir="ltr">Published in: Soft Computing<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00500-023-08322-6" target="_blank">https://dx.doi.org/10.1007/s00500-023-08322-6</a></p>2023-05-21T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00500-023-08322-6https://figshare.com/articles/journal_contribution/Stacking-based_ensemble_learning_for_remaining_useful_life_estimation/24980811CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/249808112023-05-21T03:00:00Z |
| spellingShingle | Stacking-based ensemble learning for remaining useful life estimation Begum Ay Ture (17773170) Engineering Aerospace engineering Information and computing sciences Data management and data science Machine learning Remaining useful life Ensemble learning Deep learning Stacking ensemble learning |
| status_str | publishedVersion |
| title | Stacking-based ensemble learning for remaining useful life estimation |
| title_full | Stacking-based ensemble learning for remaining useful life estimation |
| title_fullStr | Stacking-based ensemble learning for remaining useful life estimation |
| title_full_unstemmed | Stacking-based ensemble learning for remaining useful life estimation |
| title_short | Stacking-based ensemble learning for remaining useful life estimation |
| title_sort | Stacking-based ensemble learning for remaining useful life estimation |
| topic | Engineering Aerospace engineering Information and computing sciences Data management and data science Machine learning Remaining useful life Ensemble learning Deep learning Stacking ensemble learning |