Adaptive diagnosis method based on gearbox unbalanced fault data
ObjectiveThe existing intelligent fault diagnosis methods face challenges, such as model training relying on a large amount of labeled data, difficulty in obtaining fault data with different occurrence probabilities, and insufficient consideration of the impact of operating conditions. To address th...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2025-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2025.01.019 |
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| _version_ | 1832586358832496640 |
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| author | TIAN Juan XIE Gang ZHANG Shun WANG Yufei |
| author_facet | TIAN Juan XIE Gang ZHANG Shun WANG Yufei |
| author_sort | TIAN Juan |
| collection | DOAJ |
| description | ObjectiveThe existing intelligent fault diagnosis methods face challenges, such as model training relying on a large amount of labeled data, difficulty in obtaining fault data with different occurrence probabilities, and insufficient consideration of the impact of operating conditions. To address these challenges, a novel gearbox diagnosis method for adaptive inter-class and intra-class unbalanced fault data under varying working conditions was proposed.MethodsFirstly, a gated local connection network was utilized to reduce the reliance on the labeled data and extract intrinsic features directly from the original data. Secondly, a parallel mechanism of external and internal attention was designed to consider the distribution differences among inter-class and intra-class faults under different working conditions, adjusting the weights of extracted features accordingly. Finally, focal loss function was employed to focus on minority and challenging samples, enabling high-quality mining of unbalanced diagnostic information.ResultsThe proposed method is demonstrated by six unbalanced gearbox datasets, which shows great effectiveness and superiority in identifying unbalanced fault data. |
| format | Article |
| id | doaj-art-a8d57960e45f4e63a4eb25dc64e12375 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-a8d57960e45f4e63a4eb25dc64e123752025-01-25T19:00:16ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392025-01-014915316275892667Adaptive diagnosis method based on gearbox unbalanced fault dataTIAN JuanXIE GangZHANG ShunWANG YufeiObjectiveThe existing intelligent fault diagnosis methods face challenges, such as model training relying on a large amount of labeled data, difficulty in obtaining fault data with different occurrence probabilities, and insufficient consideration of the impact of operating conditions. To address these challenges, a novel gearbox diagnosis method for adaptive inter-class and intra-class unbalanced fault data under varying working conditions was proposed.MethodsFirstly, a gated local connection network was utilized to reduce the reliance on the labeled data and extract intrinsic features directly from the original data. Secondly, a parallel mechanism of external and internal attention was designed to consider the distribution differences among inter-class and intra-class faults under different working conditions, adjusting the weights of extracted features accordingly. Finally, focal loss function was employed to focus on minority and challenging samples, enabling high-quality mining of unbalanced diagnostic information.ResultsThe proposed method is demonstrated by six unbalanced gearbox datasets, which shows great effectiveness and superiority in identifying unbalanced fault data.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2025.01.019Fault diagnosisInter-class and intra-class imbalancesGated local connection networkAttention parallel mechanismFocal loss |
| spellingShingle | TIAN Juan XIE Gang ZHANG Shun WANG Yufei Adaptive diagnosis method based on gearbox unbalanced fault data Jixie chuandong Fault diagnosis Inter-class and intra-class imbalances Gated local connection network Attention parallel mechanism Focal loss |
| title | Adaptive diagnosis method based on gearbox unbalanced fault data |
| title_full | Adaptive diagnosis method based on gearbox unbalanced fault data |
| title_fullStr | Adaptive diagnosis method based on gearbox unbalanced fault data |
| title_full_unstemmed | Adaptive diagnosis method based on gearbox unbalanced fault data |
| title_short | Adaptive diagnosis method based on gearbox unbalanced fault data |
| title_sort | adaptive diagnosis method based on gearbox unbalanced fault data |
| topic | Fault diagnosis Inter-class and intra-class imbalances Gated local connection network Attention parallel mechanism Focal loss |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2025.01.019 |
| work_keys_str_mv | AT tianjuan adaptivediagnosismethodbasedongearboxunbalancedfaultdata AT xiegang adaptivediagnosismethodbasedongearboxunbalancedfaultdata AT zhangshun adaptivediagnosismethodbasedongearboxunbalancedfaultdata AT wangyufei adaptivediagnosismethodbasedongearboxunbalancedfaultdata |