Real Time Predictive Maintenance of Collaborative Robotic Arms Using a Physics Informed Digital Twin and Agentic Edge Computing

The integration of Industry 5.0 principles necessitates a transition from reactive to proactive maintenance models in automated manufacturing. This research addresses the critical challenge of synchronization errors caused by latency in cyber-physical systems by introducing a high-fidelity, Physics-Informed Digital Twin (P-DT) approach for real-time predictive maintenance of industrial robotic arms. The proposed architecture utilizes an Agentic Edge Computing layer to offload the computationally intensive task processing from the cloud, ensuring sub-millisecond processing of multi-modal sensor data such as torque, vibration, and thermal patterns. A new Deep Reinforcement Learning (DRL) method is also developed at the edge to optimize resource allocation and predict Remaining Useful Life (RUL) under stochastic workloads. By embedding physical kinematic constraints within the neural network’s loss function, this approach shows a 28% relative improvement in fault detection sensitivity over traditional data-driven models. Simulation results in dense industrial settings show that the P-DT approach preserves a 99.7% level of synchronization accuracy while lowering unplanned downtime by 34%. Additionally, the approach facilitates proactive self-correction capabilities to compensate for mechanical degradation by adjusting motion plans. These results offer a sound solution for the deployment of autonomous and self-healing robotic systems in latency-sensitive industrial settings.