Physics-Informed Transformer with ODE-Guided Joint Modeling for Fault Classification and RUL Prediction in Collaborative Robots

Abstract

Accurate early diagnosis of fault types and Remaining Useful Life (RUL) prediction are critical for predictive maintenance in collaborative robotic systems, especially under limited labeled data conditions. This paper proposes PhysODE-Joint, a physics-informed deep learning framework that unifies Transformer-based temporal modeling with fault-specific ODE-guided degradation dynamics for joint fault classification and RUL estimation. The method incorporates domain knowledge of mechanical wear and thermal degradation into the feature learning process and employs a cascade architecture to ensure physical plausibility and class-aware prediction. A hybrid training strategy is introduced, integrating limited real-world sensor data with synthetic degradation sequences generated from physics-based models. Experimental results on real-world robotic datasets demonstrate that PhysODE-Joint significantly outperforms conventional data-driven models, particularly in small-sample scenarios, offering a robust solution for health monitoring and maintenance scheduling.