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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 1004-2539 |