Data-Driven Failure Prediction and Condition Monitoring of the A2 Lower-Arm Servo Motor in a Vertically Articulated Industrial Robot

Abstract

The growing demand for high precision and uninterrupted production in automotive manufacturing necessitates reliable failure prediction strategies for industrial robotic systems. In vertically articulated robots, the Axis-2 lower-arm servo motor is subjected to significant dynamic loading, thermal stress, and mechanical vibration, making it particularly vulnerable to premature degradation. Conventional control systems typically detect faults only after performance deterioration becomes critical, limiting opportunities for preventive intervention. This study proposes a multi-parameter condition monitoring framework for early failure prediction of the A2 lower-arm servo motor. Real-time measurements of motor current, surface temperature, and vibration velocity were acquired using externally mounted sensors under full-load operating conditions. Additional brake and resolver data were periodically evaluated to support system assessment. The collected operational dataset was analyzed using a Random Forest regression model to identify degradation patterns and estimate remaining service life. The results indicate that combined monitoring of electrical, thermal, and mechanical indicators enhances diagnostic reliability and enables early identification of abnormal operating trends. The proposed data-driven approach supports predictive maintenance planning, reduces unexpected downtime, and improves operational stability in industrial robotic applications.