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Semi-supervised gearbox fault diagnosis under variable working conditions based on masked contrastive learning
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Huiyun ZHANG1, Fangjun ZUO1, Hang LI1, Xi YU2
Journal of Mechanical Strength | 2025, 47(6) : 72 - 81
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Journal of Mechanical Strength | 2025, 47(6): 72-81
Vibration·Noise·Monitoring·Diagnosis
Semi-supervised gearbox fault diagnosis under variable working conditions based on masked contrastive learning
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Huiyun ZHANG1, Fangjun ZUO1, Hang LI1, Xi YU2
Affiliations
  • 1.School of Intelligent Manufacturing, Chengdu Technological University, Chengdu 610031, China
  • 2.School of Mechanical Engineering, Sichuan University, Chengdu 610065, China
Published: 2025-06-15 doi: 10.16579/j.issn.1001.9669.2025.06.009
Outline
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To address the problem that it is difficult to label variable working condition gearbox fault samples and the significant data distribution discrepancies in practical engineering, which result in reduced accuracy of fault diagnosis models,a semi-supervised gearbox fault diagnosis method based on masked contrastive learning is proposed. Firstly, a random mask was used to hide part of the information in the unlabeled dataset, generating two different masked instances for each unlabeled sample. Secondly, a dynamic convolutional neural network was employed to dynamically weight and aggregate the masked instances, enabling discriminative feature modeling of different masked instances. Then, a contrastive learning framework was constructed with the optimization goal of maximizing the similarity between features of different masked instances. By enhancing the consistency of feature representations of masked instance pairs, the model's dependency on labels was reduced. Finally, during the fine-tuning phase, a domain-conditioned feature correction strategy was introduced to generate target domain feature corrections. By aligning source domain features and target domain corrected features according to the metric of minimizing domain feature distribution discrepancies, the method explicitly reduces the domain distribution differences caused by varying working conditions. Validation on a variable working condition gearbox fault dataset demonstrates the effectiveness of the proposed method.

Gearbox  /  Variable working condition  /  Fault diagnosis  /  Contrastive learning  /  Semi-supervised
Huiyun ZHANG, Fangjun ZUO, Hang LI, Xi YU. Semi-supervised gearbox fault diagnosis under variable working conditions based on masked contrastive learning[J]. Journal of Mechanical Strength, 2025 , 47 (6) : 72 -81 . DOI: 10.16579/j.issn.1001.9669.2025.06.009
  • Sichuan Provincial Natural Science Foundation(24NSFSC1295)
Year 2025 volume 47 Issue 6
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Article Info
doi: 10.16579/j.issn.1001.9669.2025.06.009
  • Receive Date:2024-07-24
  • Online Date:2026-03-18
  • Published:2025-06-15
Article Data
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History
  • Received:2024-07-24
  • Revised:2024-11-04
Funding
Sichuan Provincial Natural Science Foundation(24NSFSC1295)
Affiliations
    1.School of Intelligent Manufacturing, Chengdu Technological University, Chengdu 610031, China
    2.School of Mechanical Engineering, Sichuan University, Chengdu 610065, China

Corresponding:

ZUO Fangjun, E-mail:
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表12种不同金属材料的力学参数

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Number of
genus
种数
Number of
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占总种数比例
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Number of
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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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