An Integrated Ensemble Learning Model for Imbalanced Fault Diagnostics and Prognostics
With the development of artificial intelligence technology, data-driven fault diagnostics and prognostics in industrial systems have been a hot research area since the large volume of industrial data is being collected from the industrial process. However, imbalanced distributions exist pervasively...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8295035/ |
| _version_ | 1818331904691142656 |
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| author | Zhenyu Wu Wenfang Lin Yang Ji |
| author_facet | Zhenyu Wu Wenfang Lin Yang Ji |
| author_sort | Zhenyu Wu |
| collection | DOAJ |
| description | With the development of artificial intelligence technology, data-driven fault diagnostics and prognostics in industrial systems have been a hot research area since the large volume of industrial data is being collected from the industrial process. However, imbalanced distributions exist pervasively between faulty and normal samples, which leads to imprecise fault diagnostics and prognostics. In this paper, an effective imbalance learning algorithm Easy-SMT is proposed. Easy-SMT is an integrated ensemble-based method, which comprises synthetic minority oversampling technique (SMOTE)-based oversampling policy to augment minority faulty classes and EasyEnsemble to transfer an imbalanced class learning problem into an ensemble-based balanced learning subproblem. We validate the feasibility and effectiveness of the proposed method in a real wind turbine failure forecast challenge, and our solution has won the third place among hundreds of teams. Moreover, we also evaluate the method on prognostics and health management 2015 challenge datasets, and the results show that the model could also achieve good performance on multiclass imbalance learning task compared with baseline classifiers. |
| first_indexed | 2024-12-13T13:27:16Z |
| format | Article |
| id | doaj.art-22de2aa0b6eb4b189e1f86d47ed6fef5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:27:16Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-22de2aa0b6eb4b189e1f86d47ed6fef52022-12-21T23:44:16ZengIEEEIEEE Access2169-35362018-01-0168394840210.1109/ACCESS.2018.28071218295035An Integrated Ensemble Learning Model for Imbalanced Fault Diagnostics and PrognosticsZhenyu Wu0https://orcid.org/0000-0001-9617-7094Wenfang Lin1Yang Ji2Engineering Research Center of Information Network, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaKey Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaKey Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaWith the development of artificial intelligence technology, data-driven fault diagnostics and prognostics in industrial systems have been a hot research area since the large volume of industrial data is being collected from the industrial process. However, imbalanced distributions exist pervasively between faulty and normal samples, which leads to imprecise fault diagnostics and prognostics. In this paper, an effective imbalance learning algorithm Easy-SMT is proposed. Easy-SMT is an integrated ensemble-based method, which comprises synthetic minority oversampling technique (SMOTE)-based oversampling policy to augment minority faulty classes and EasyEnsemble to transfer an imbalanced class learning problem into an ensemble-based balanced learning subproblem. We validate the feasibility and effectiveness of the proposed method in a real wind turbine failure forecast challenge, and our solution has won the third place among hundreds of teams. Moreover, we also evaluate the method on prognostics and health management 2015 challenge datasets, and the results show that the model could also achieve good performance on multiclass imbalance learning task compared with baseline classifiers.https://ieeexplore.ieee.org/document/8295035/Industrial prognosticsclass-imbalance learningmachine learningensemble learning |
| spellingShingle | Zhenyu Wu Wenfang Lin Yang Ji An Integrated Ensemble Learning Model for Imbalanced Fault Diagnostics and Prognostics IEEE Access Industrial prognostics class-imbalance learning machine learning ensemble learning |
| title | An Integrated Ensemble Learning Model for Imbalanced Fault Diagnostics and Prognostics |
| title_full | An Integrated Ensemble Learning Model for Imbalanced Fault Diagnostics and Prognostics |
| title_fullStr | An Integrated Ensemble Learning Model for Imbalanced Fault Diagnostics and Prognostics |
| title_full_unstemmed | An Integrated Ensemble Learning Model for Imbalanced Fault Diagnostics and Prognostics |
| title_short | An Integrated Ensemble Learning Model for Imbalanced Fault Diagnostics and Prognostics |
| title_sort | integrated ensemble learning model for imbalanced fault diagnostics and prognostics |
| topic | Industrial prognostics class-imbalance learning machine learning ensemble learning |
| url | https://ieeexplore.ieee.org/document/8295035/ |
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