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LSGAN-Swin Transformer diagnosis method of bearing fault under unbalanced samples
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Jie LIU, Yutao TAN, Yanling GU, Na YANG
Journal of Vibration Engineering | 2025, 38(8) : 1775 - 1787
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Journal of Vibration Engineering | 2025, 38(8): 1775-1787
LSGAN-Swin Transformer diagnosis method of bearing fault under unbalanced samples
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Jie LIU, Yutao TAN, Yanling GU, Na YANG
Affiliations
  • School of Mechanical Engineering，Shenyang University of Technology，Shenyang 110870，China
Published: 2025-08-10 doi: 10.16385/j.cnki.issn.1004-4523.202308023
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Aiming at the problems of bearings working in complex environments，where fault data are difficult to obtain in large quantities and the serious imbalance between the ratio of normal data and fault data resulting in insufficient in-depth model training and low diagnostic accuracy，a bearing fault diagnosis method based on LSGAN-Swin Transformer is proposed. The least-squares generative adversarial network is utilized to expand the imbalanced or lack of bearing dataset，and the windowed self-attentive network is introduced for bearing fault state identification. The proposed method is validated by using two date sets，and compared with SGAN and WGAN respectively. It is demonstrated that LSGAN generates data training models with higher accuracy. The proposed Swin Transformer （Swin-T） model is compared with CNN，AlexNet and SqueezeNet under small sample conditions，and the accuracy is improved by 34.85%，13.45%，and 12.95%，respectively. The classification effect of the model is evaluated by t-SNE visualization，and the results show that the LSGAN-Swin-T model can still meet the requirements in fault diagnosis better when the number of training samples is small，which provides a new idea for the research of bearing fault diagnosis under unbalanced data.

fault diagnosis  /  rolling bearings  /  unbalanced sample  /  least square generative adversarial network  /  shifted windows transformer （Swin Transformer）
Jie LIU, Yutao TAN, Yanling GU, Na YANG. LSGAN-Swin Transformer diagnosis method of bearing fault under unbalanced samples[J]. Journal of Vibration Engineering, 2025 , 38 (8) : 1775 -1787 . DOI: 10.16385/j.cnki.issn.1004-4523.202308023
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Year 2025 volume 38 Issue 8
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.202308023
  • Receive Date：2023-08-11
  • Online Date：2026-02-09
  • Published：2025-08-10
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  • Received：2023-08-11
  • Revised：2023-11-06
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    School of Mechanical Engineering，Shenyang University of Technology，Shenyang 110870，China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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