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The application of box graph and feature fusion model in the classification of wheel set bearing label confusion data
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Xiong ZHANG1, 2, Jialu LI2, Fan DONG2, Wenbo WU2, Shuting WAN1, 2, Xiaohui GU3
Journal of Vibration Engineering | 2025, 38(1) : 88 - 95
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Journal of Vibration Engineering | 2025, 38(1): 88-95
The application of box graph and feature fusion model in the classification of wheel set bearing label confusion data
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Xiong ZHANG1, 2, Jialu LI2, Fan DONG2, Wenbo WU2, Shuting WAN1, 2, Xiaohui GU3
Affiliations
  • 1.Hebei Key Laboratory of Electric Machinery Health Maintenance & Failure Prevention, Baoding 071003, China
  • 2.Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
  • 3.State Key Laboratory of Mechanics Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Published: 2025-01-10 doi: 10.16385/j.cnki.issn.1004-4523.2025.01.010
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Deep learning methods have shown great potential in the field of fault diagnosis of train wheelset bearings, but their effective implementation is based on the correct matching between various types of data and category labels. For data with a small number of label error samples, traditional deep learning methods are difficult to achieve the expected diagnostic effect. To address this issue, this paper proposes a fault diagnosis method combining box graph method and feature fusion model is proposed to address this issue. In this method, the outlier in each group of bearing signals is removed by box graph method, and the remaining data is expanded by the SMOTE method to restore to the original data size; Input the processed sample data into the improved feature fusion model for fault identification and classification. The experimental data of train wheel bearings was used for validation. The results showed that compared to directly using traditional neural network models for fault diagnosis, the diagnostic accuracy of the method proposed in this paper is higher, indicating that the method has better processing performance for bearing data with a small number of label error samples.

fault diagnosis  /  wheel set bearings  /  label error  /  feature fusion  /  box graph
Xiong ZHANG, Jialu LI, Fan DONG, Wenbo WU, Shuting WAN, Xiaohui GU. The application of box graph and feature fusion model in the classification of wheel set bearing label confusion data[J]. Journal of Vibration Engineering, 2025 , 38 (1) : 88 -95 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.01.010
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Year 2025 volume 38 Issue 1
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.2025.01.010
  • Receive Date：2023-05-10
  • Online Date：2026-02-11
  • Published：2025-01-10
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  • Received：2023-05-10
  • Revised：2023-08-05
Funding
Affiliations
    1.Hebei Key Laboratory of Electric Machinery Health Maintenance & Failure Prevention, Baoding 071003, China
    2.Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
    3.State Key Laboratory of Mechanics Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, 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
species (%)
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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