报告详情
Inverse Physics-Constrained Learning for Robust Fault Diagnosis in Robotic Joint Transmission Systems
编号：51
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更新：2025-06-15 10:47:48 浏览：14次
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报告开始：暂无开始时间（Asia/Shanghai）
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摘要
Accurate and interpretable fault diagnosis for robotic joint transmission systems remains a key bottleneck in achieving reliable industrial automation. These systems often degrade through complex time-varying dynamics—such as stiffness loss and damping fluctuations—that challenge conventional black-box models, especially when signal noise and structural ambiguity are involved. To overcome this, we propose an inverse Physics-Constrained Learning (iPCL) framework that infers latent mechanical parameters from vibration signals using a simplified dynamic model. A residual physics constraint is introduced to align estimated responses with system dynamics, offering physically consistent supervision without relying on hard-to-measure forces. In parallel, motor current signals are leveraged as auxiliary inputs to enhance class separability, decoupled from the physics pathway to preserve interpretability. The fused representation significantly improves both classification accuracy and physical insight. Experimental results on real-world joint datasets demonstrate that iPCL consistently outperforms traditional signal-driven and deep learning baselines. This work establishes a scalable and physically grounded diagnostic paradigm for intelligent health monitoring in industrial robotic transmissions.
关键词
Physics-guided fault diagnosis, Robotic joint transmission, Residual-constrained learning, Latent degradation modeling, Multi-modal sensor
报告人
Qiao Han
Ph.D. Student South China University of Technology；Institute for Super Robotics（Huangpu）稿件作者
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重要日期
-
会议日期
08月01日
2025
至08月04日
2025
-
06月23日 2025
初稿截稿日期
主办单位
中国机械工程学会设备智能运维分会
承办单位
新疆大学
联系方式
- Mrs. Yanruqiang
- ya******@xjtu.edu.cn
历届会议
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2025年08月01日 中国 wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023年09月21日 中国 Hefei
第一届（国际）设备智能运维大会
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