Anomaly Monitoring Model of Industrial Processes Based on Graph Similarity and Applications

Aiming at the strong spatio-temporal coupling relationship between data in the actual industrial production process, which leads to the problem of insufficient reliability and poor timeliness of traditional process anomaly monitoring methods, a time series anomaly monitoring model based on the graph similarity network with multi-scale features is proposed, which can react to the anomalies in the process in a timely and effective manner to guarantee the production safety.First, a graph-building method for spatio-temporally coupled time-series data using multidimensional time-varying feature map embedding is designed to capture the data’s dependence on time, while the topology of the graph is utilized to learn the spatial coupling of the data; second, a graph similarity-based anomaly monitoring strategy is innovatively proposed to measure the anomalies of the process using the difference degree index between the standard normal process data and the monitoring data. Finally, the proposed method is validated using the standard normal operating condition data of the Tennessee-Eastman (TE) process as well as the standard fault data. The experimental results show that the proposed model can identify anomalies more quickly and accurately than other typical methods, which significantly improves the reliability and timeliness of industrial process anomaly monitoring.