Data-driven edge intelligence two-axis servo system: multimodal end-to-end friction diagnosis

Abstract

Industrial dual-axis servo systems serve as the core actuators for achieving precise positioning in high-end equipment such as intelligent robots and precision manipulation robots; therefore, their control accuracy directly affects the precision and stability of the robot’s end-effector operations. However, the inherent nonlinear friction in guide rails (the Striebeck effect) can cause single-axis tracking errors or dual-axis contour distortion, thereby affecting the performance of micro- and nanomanipulation as well as precision assembly. This paper proposes a physics-based, edge AI-driven multimodal fault diagnosis method. Specifically, low-frequency armature current and high-frequency vibration signals are effectively fused, and a heterogeneous multimodal representation is constructed using high-fidelity dynamic simulation methods at the system’s perception layer, thereby achieving exceptional noise resistance. Extensive robustness analysis demonstrates the framework’s resilience to signal noise, ensuring reliable deployment in dynamic industrial environments. Simulation results indicate that the proposed framework achieves a diagnostic accuracy of 98.73%, significantly outperforming single-modal baselines (40.39%/77.03%). The proposed end-to-end method avoids the need for complex manual feature engineering, features low computational complexity and high deployment convenience, and meets the stringent computational constraints of embedded robot controllers. Furthermore, with its extremely low computational complexity, this algorithm provides an excellent solution to fault diagnosis in precision motion control for intelligent robots.