Fault Diagnosis in Robot Drive Systems Using Data-Driven Dynamics Learning

Reliable fault diagnosis in industrial robots is essential for minimizing downtime and ensuring safe operations. Conventional model-based methods often require detailed system knowledge and struggle with unmodeled dynamics, while purely data-driven approaches can achieve good accuracy but may not fully exploit the underlying structure of robot motion. In this study, we propose a feature-informed machine learning framework for fault detection in robotic manipulators. A multi-layer perceptron (MLP) is trained to estimate robot dynamics from joint states, and SHapley Additive exPlanations (SHAP) values are computed to derive discriminative feature representations. These attribution patterns, or SHAP fingerprints, serve as enhanced descriptors that enable reliable classification between normal and faulty operating conditions. Experiments were conducted using real-world data collected from industrial robots, covering both motor brake faults and reducer anomalies. The proposed SHAP-informed framework achieved nearly perfect classification performance (0.998 ± 0.003), significantly outperforming baseline classifiers that relied only on raw kinematic features (0.925 ± 0.002). Moreover, the SHAP-derived representations revealed fault-consistent patterns, such as enhanced velocity contributions under frictional effects and joint-specific shifts for reducer faults. The results demonstrate that the proposed method provides high diagnostic accuracy and robust generalization, making it well suited for safety-critical applications and predictive maintenance in industrial robotics.