Detection and Monitoring for Anomalies and Degradation of a Robotic Arm Using Machine Learning

Robotic arm performance varies due to normal and abnormal events. Normal events may include degradation of equipment, motors, mechanical system joints, and gears, while abnormal events may occur such as faulty episodes. In this paper, we address positional performance degradation that can be stopped and redressed if suitable required action is achieved. The Tool Center Point (TCP) position measurement devices are expensive, hence unavailable to every robot. Some industrial processes are critically sensitive to target tool position such as assembly, pin and past, and material handling. We propose a data driven artificial intelligence tool to detect anomalies and degradation of the robotic arm for a positional health assessment without the need for special advanced sensors. TCP deviation is predicted using deep machine learning models that train on time series of historical data of the robot’s performance. Statistical thresholds are calculated to detect the robotic arm’s degradation and anomalies by performing residual analysis. An alarm system is built by applying the proposed monitoring tool online.