A deep graph neural network-based link prediction model for proactive anomaly detection in discrete manufacturing workshop

Abstract

Production anomaly has always been one of the main influencing factors that prevent discrete manufacturing workshops from maintaining stability and agility. Proactive anomaly detection can evaluate the production state and serves as a crucial foundation for preventive maintenance decision. Knowledge graph enables the use of multi-source manufacturing data as a data foundation for proactive anomaly detection. Although rich manufacturing data can comprehensively depict complex manufacturing process, constructing an accurate proactive anomaly detection model remains challenging because of insufficient analysis of the local and temporal features of the manufacturing process. This paper presents a link prediction model based on a deep graph neural network to solve the problem. Specifically, the manufacturing knowledge graph is constructed through OPC UA information model, Bert model and OWL semantic mapping model to organize multi-source heterogeneous data. The deep autoencoder model with local graph learning and the Seq2Seq model with attention mechanism are trained to analyze the neighboring relationship and the temporal correlation of the manufacturing elements, respectively. Finally, the link prediction model is designed by integrating both local and temporal features, with a restructured loss function to improve training effectiveness. Experiments suggest that the designed link prediction model has better prediction performance and is at least 25.6 % higher than the baseline models on the mean reciprocal rank.