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Multi-source fusion perception intelligent fault diagnosis methods for critical components of industrial robots
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Qiao HAN1, 2, Jing LIU1, Guolin HE1, Weihua LI1
Journal of Vibration Engineering | 2025, 38(6) : 1252 - 1259
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Journal of Vibration Engineering | 2025, 38(6): 1252-1259
Multi-source fusion perception intelligent fault diagnosis methods for critical components of industrial robots
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Qiao HAN1, 2, Jing LIU1, Guolin HE1, Weihua LI1
Affiliations
  • 1.School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510641,China
  • 2.Institute for Super Robotics (Huangpu),Guangzhou 510700,China
Published: 2025-06-10 doi: 10.16385/j.cnki.issn.1004-4523.2025.06.013
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Key components of industrial robots are prone to early-stage performance degradation under complex operating conditions, characterized by strongly non-stationary responses and significant heterogeneity across sensing channels. Traditional diagnostic methods struggle with robust and interpretable fusion of multi-source information, limiting their practical deployment. This paper proposes a dual-channel intelligent diagnostic method for robotic transmission mechanisms, integrating physics-driven sensitivity weighting and residual uncertainty compensation (RUC). Specifically, vibration and torque signals, representing structural response and driving excitation respectively, are selected due to their distinct temporal scales and complementary physical characteristics. A three-layer mapping (fault type-dynamic response characteristic-sensing channel) is constructed to quantify channel dominance for different fault modes. Then, a multi-scale sensitivity evaluation mechanism based on signal-to-noise ratio (SNR), modulation index (MI), and kurtosis guides adaptive weight allocation, while the RUC strategy enhances the expression of features from weakly dominant channels, improving fusion stability. Finally, a physically interpretable and lightweight diagnostic framework is established. Experiments conducted on a public gearbox dataset validate that the proposed method provides superior diagnostic accuracy, interpretability, and deployment potential, demonstrating significant promise for physically consistent multi-source fusion diagnosis in robotic transmission systems.

industrial robots  /  gear transmission system  /  multi-source fusion  /  signal mapping  /  intelligent fault diagnosis
Qiao HAN, Jing LIU, Guolin HE, Weihua LI. Multi-source fusion perception intelligent fault diagnosis methods for critical components of industrial robots[J]. Journal of Vibration Engineering, 2025 , 38 (6) : 1252 -1259 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.06.013
Year 2025 volume 38 Issue 6
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.2025.06.013
  • Receive Date:2025-04-30
  • Online Date:2026-02-12
  • Published:2025-06-10
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  • Received:2025-04-30
  • Revised:2025-05-27
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    1.School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510641,China
    2.Institute for Super Robotics (Huangpu),Guangzhou 510700,China
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表12种不同金属材料的力学参数

Family
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Number of
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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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