To ensure robotic system safety and reliability, accurate tracking of robotic arm and human joint positions and real-time monitoring of joint changes and early signs of wear or loosening are essential. Traditional methods suffer from limited accuracy and high maintenance costs in complex environments due to similar components, occlusion, and complicated backgrounds. To address these challenges, this paper proposes a fault detection method for robotic arms in complex scenarios that enhances joint identification and early warning capabilities. The approach integrates colour detection, geometric perception, the Detect DBB module, and the integrated time processing (ITP) module. Leveraging deep feature learning, it enables real-time trajectory monitoring and precise identification of early wear and loosening. Experiments demonstrate that the proposed method improves detection accuracy, reduces unplanned downtime, extends the robotic arm's service life, and lowers maintenance costs, thus validating its effectiveness and practical value.
Xu et al. (Wed,) studied this question.