JoWUA

Abstract

The success and the importance of industrial robotic arms in contemporary automation rely on their accuracy, velocity, and capability of functioning under varied conditions, but mechanical and electromechanical deteriorations, including bearing wear, gear backlash, joint misalignment, and imbalance of motor torque, may negatively affect performance, safety, and production unless inner problems are revealed early. The traditional methods of maintenance, such as reactive repairs and fixed-interval inspections, are inefficient and do not capture the faults in their early stages, whereas the current machine learning-based solutions typically process sensor signals separately, neglecting inter-joint kinematic constraints, and are not physically interpretable, leading to poor performance in cases of rare or hidden faults. This paper will solve these challenges by introducing a Hierarchical Temporal-Graph Physics-Guided Fusion Network (HT-GP-FusionNet) in predictive fault diagnosis of robotic arms. The framework incorporates hierarchical temporal modeling to decompose short-term and long-term dynamics in multi-sensor data consisting of accelerometers, gyroscopes, motor currents, and joint positions, and a graph neural network fully captures the inter-joint relationships and fault propagation along the kinematic chain. It uses a physics-based regularization that requires consistency with motion equations and energy conservation laws, and generates samples of faults to be generated by a generative fault augmentation model to promote few-shot generalization. The experimental results on a set of 50,000 sequences (10 sensors, five fault types) sampled at 1 kHz show that HT-GP-FusionNet outperforms CNN (89.3), LSTM (90.5), CNN-LSTM (92.8), and GNN-based (93.6) models in all metrics and has higher recall with rare faults, and can be used when the number is unbalanced. These conclusions have been proved by ablation experiments that show temporal modeling, graph reasoning, physics-guided regularization, and data augmentation make significant contributions to performance. In general, HT-GP-FusionNet offers a scalable, interpretable, and physics-consistent solution to the early and accurate predictive maintenance, hence higher reliability, safety, and operational efficiency during industrial robotic systems.