Learning Based Anomaly Detection for Industrial Arm Applications

Smart Manufacturing (SM) is envisioned to make manufacturing processes more efficient through automation and integration of networked information systems. Robotic arms are integral to this vision. However the benefits of SM, enabled by automation and networking, also come with cyber risks. In this work, we propose an anomaly detection framework for robotic arms in a manufacturing pipeline and integrate it into Robot Operating System (ROS), a middleware framework whose variants are being considered for deployment in industrial environments for flexible automation. In particular, we explore whether the repetitive behavior of an industrial arm can be leveraged to detect anomalous behaviour that may indicate an intrusion. Based on a learned model, we classify a robot's actions as anomalous or benign. We introduce the notion of a 'tolerance envelope' to train a supervised learning model. Our empirical evaluation shows that anomalies that take the robot out of pre-determined tolerance levels can be detected with high accuracy.