Graph Anomaly Detection in Programmable Logic Controllers Based on Service Computing
Huifeng Wu, Junjie Hu, Xi Tian, Zeyun Xiao, Danfeng Sun, René Simon · 2024
The rise of smart factories is driving the complete automation of manufacturing environments, making anomaly detection a more important task than ever. With highly intelligent equipment deployed on fully-automated production lines, even a slight anomaly may seriously impact the entire manufacturing process. However, most industrial data containing slight anomalous features exhibits strong correlations that are not fully exploited by existing machine learning models. In general, the anomaly detection model needs to run within programmable logic controllers (PLCs) since it is the main controller of the intelligent equipment, and PLCs have limited resources making it difficult to execute larger models effectively. To address the challenge, we propose a graph anomaly detection method in programmable logic controllers based on service computing (PCSC). The model establishes a data relationship graph through clustering and then feeds it into a neural network with a symmetric structure consisting of graph convolutional layers and LSTM units. This approach enables the analysis of correlations within industrial data and facilitates the extraction of slight abnormal features. For efficient execution of the model, we establish service computing nodes in the PLCs that support model splitting. We tested the model on several publicly available datasets and an actual dataset from an injection molding production line. The results show that the model performs well on different datasets.