Knowledge-Augmented Anomaly Detection in Small Lot Production for Semantic Temporal Process Data.

To mitigate unforeseen operational interruptions caused by potential malfunctions in robotic systems employed in industrial automation, we propose an innovative strategy for anomaly detection that incorporates a Transformer-based reconstruction network for identifying irregularities in skill-oriented manufacturing. Leveraging a semantic representation of processes, products, and resources, a semantic manufacturing execution system synthesizes an appropriate robot program and carries out the process. Our technique utilizes these descriptions to partition and automatically assign pertinent process data, facilitating the automated configuration of the anomaly detection pipeline. To overcome limited data availability, we employ a sliding window technique for data augmentation and capitalize on the attention mechanism of the Transformer to effectively extract semantic interdependencies from the time series data. By examining the discrepancies between the reconstructed time series data and the original, we can detect anomalies related to the manufacturing process. Through experiments conducted on an actual robot workcell, we demonstrate that our approach surpasses alternative competitive concepts.