Industrial Robot Condition Monitoring Using Different Motor Current Signals

For cost-effective and accurate monitoring and diagnosis of industrial robots, Motor Current Signature Analysis (MCSA) is examined for detecting joint load variations in the UR5. The motor current signals from both an external high performance current clamp and the internal controller are modeled to understand the fundamentals of motor signal responsiveness to joint load changes caused by faults in transmission systems. Experiments introducing abnormal loadings via rubber bands affixed to the robot joints were conducted to evaluate the effectiveness of both the current clamp and the internal controller current signals as proxies for these load modifications. Results underscore that MCSA can provide good capabilities for cobot joint surveillance by the overall root mean square values and average peak values over direction changes. However, preliminary observations suggest that while the internal controller current signals offer beneficial modulation insights, they are hindered diagnostically due to the omission of phase information. The study concludes by advocating the integration of supplementary sensors and improved signal processing approaches to augment the diagnostic prowess of controller-derived currents.