Aiming at the problems of spatial coupling between process parameters and time dependence in actual industrial processes, which lead to insufficient reliability and poor timeliness of process anomaly monitoring, a predictive model of time series graph slicing networks based on dynamic multi-temporal features is proposed, which can effectively reduce false alarms and omissions in process anomalies, and explain the causes of anomalies at the same time. First, a method for extracting and building graphs of multidimensional spatio-temporally coupled time series data based on data dimensionality reduction method and causality calculation is designed to mine the potential associations between strongly coupled process parameters. Second, a dynamic multi-temporal feature graph embedding strategy and a two-channel graph structure learning network are proposed to effectively solve the problem that individual time-dimension features cannot fully utilize the continuity and sequentiality in the time dimension, and to realize the long-time dynamic perception of global variables.Finally, the prediction performance of the proposed method as well as the anomaly monitoring performance is validated using the time series dataset generated by the Matlab-based Tennessee Eastman (TE) process platform as well as the SWaT process dataset.The experimental results show that the proposed model has an average fitting degree of more than 0.99 for the prediction of target variables, which can identify and explain the anomalies more quickly and accurately compared with other typical methods, and significantly improves the reliability and timeliness of the anomaly monitoring of industrial processes.

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 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 dependence of the data 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.

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.