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Wheelset Bearing Fault Detection Based on Multi‑resolution Siamese Network
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Feiyue DENG1, Yan BI1, Yongqiang LIU1, Chunxue SONG2, Rujiang HAO1
Journal of Vibration,Measurement and Diagnosis | 2025, 45(5) : 1001 - 1007
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Journal of Vibration,Measurement and Diagnosis | 2025, 45(5): 1001-1007
• PAPER •
Wheelset Bearing Fault Detection Based on Multi‑resolution Siamese Network
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Feiyue DENG1, Yan BI1, Yongqiang LIU1, Chunxue SONG2, Rujiang HAO1
Affiliations
  • 1.School of Mechanical Engineering，Shijiazhuang Tiedao University　Shijiazhuang，050043，China
  • 2.CRRC Shijiazhuang Co.，Ltd.　Shijiazhuang，050043，China
Published: 2025-10-01 doi: 10.16450/j.cnki.issn.1004-6801.2025.05.019
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In order to improve the fault detection performance of wheelset bearings under small sample image conditions，a machine vision inspection method based on a novel multi-resolution siamese neural network （MrSNN） is proposed for surface defect detection of wheelset bearings. First，the siamese neural network （SNN） is used as the basic model framework. A multi-resolution convolution fusion block （MrCFB） containing convolution kernels of different sizes and dilation factors is constructed to comprehensively extract the detailed features and contour features from images. Then，a dual attention mechanism combining channel and spatial information is adopted to recalibrate the multi-resolution feature weights，further enhancing the image feature extraction capability of the model. Finally，the algorithm is validated through the detection and analysis of four types of wheelset bearings images： normal，scratched，pitted and spalled. Experimental results show that the recognition rate for the three types of faulty images reaches 100%，the recognition rate for normal images is 95%，and the overall recognition accuracy is 98.75%. The recognition accuracy is superior to that of traditional SNN and YOLO-V5 models.

rolling bearing  /  fault diagnosis  /  neural network  /  image detection  /  multi-resolution feature
Feiyue DENG, Yan BI, Yongqiang LIU, Chunxue SONG, Rujiang HAO. Wheelset Bearing Fault Detection Based on Multi‑resolution Siamese Network[J]. Journal of Vibration,Measurement and Diagnosis, 2025 , 45 (5) : 1001 -1007 . DOI: 10.16450/j.cnki.issn.1004-6801.2025.05.019
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Article Info
doi: 10.16450/j.cnki.issn.1004-6801.2025.05.019
  • Receive Date：2023-03-20
  • Online Date：2026-03-27
  • Published：2025-10-01
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  • Received：2023-03-20
  • Revised：2023-08-26
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Affiliations
    1.School of Mechanical Engineering，Shijiazhuang Tiedao University　Shijiazhuang，050043，China
    2.CRRC Shijiazhuang Co.，Ltd.　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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