Unsupervised motion-based anomaly detection with graph attention networks for industrial robots labeling

Abstract

As automated labeling on products in intelligent manufacturing grows in importance, detecting anomalies in the end-effectors used for industrial robots labeling is essential for maintaining production line stability and efficiency. Considering the distinct characteristics of specific movements in the labeling process, different motions, such as moving, labeling and rolling, contribute different effects to end-effector abnormalities. It's challenging to distinguish between normal and anomalies, instead of treating all motions as a homogeneous whole. Also, real-world industrial scenarios often lack the sufficient data on abnormal states, and resource constraints limit computation efficiency. In view of this, this paper aims to develop a task-specific anomaly detection solution tailored to the distinct motions of industrial robots labeling. To achieve this goal, an unsupervised, motion-based anomaly detection framework is proposed. The raw sensor signals from each motion are segmented and a group of encoder networks are employed to extract latent representations for each motion. Then, these motions are modeled as nodes in a graph, where a feature fusion module based on a Graph Attention Network (GAT) captures the interrelationships between them. A memory-augmented reconstruction module with multi-scale skip connections enhances the model's ability to detect anomalies. Finally, an anomaly detection module identifies abnormal states of the end-effector. Experimental validations are conducted on a dataset from real-world steel coil labeling task. The results show that the proposed framework can achieve an average performance of 98.24% with an inference time of 15 ms, also demonstrating the effectiveness of its structural design and key modules.