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Inverse Physics-Constrained Learning for Robust Fault Diagnosis in Robotic Joint Transmission Systems
ID：51 View Protection：ATTENDEE Updated Time：2025-06-15 10:47:48 Hits：245 Oral Presentation

Start Time：Pending（Asia/Shanghai）

Duration：Pending

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Abstract
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.
Keywords
Physics-guided fault diagnosis, Robotic joint transmission, Residual-constrained learning, Latent degradation modeling, Multi-modal sensor
Speaker
Qiao Han
Ph.D. Student South China University of Technology；Institute for Super Robotics（Huangpu）

Submission Author
Qiao Han South China University of Technology；Institute for Super Robotics（Huangpu）
Jing Liu South China University of Technology
Yuan Zheng South China University of Technology
Hao Lan South China University of Technology
Xu Tan South China University of Technology
Weihua Li South China University of Technology
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Important Date

  • Conference Date

    Aug 01

    2025

    to

    Aug 04

    2025

  • Aug 20 2025

    Draft paper submission deadline

Sponsored By
中国机械工程学会设备智能运维分会
Organized By
新疆大学
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