A Causal-aware Spatiotemporal Network with Prediction-reconstruction for Interpretable Anomaly Monitoring in Complex Industrial Processes.

This work is developed by the research team led by Prof. Chunhua Yang and Prof. Weihua Gui.

A Causal-aware Spatiotemporal Network with Prediction-reconstruction for Interpretable Anomaly Monitoring in Complex Industrial Processes.
Bei Sun^a, Weilong Tong^a, Mingjie Lv^a,b, Fakun Zheng^c, Yucheng Ke^d
a School of Automation, Central South University.
b Department of Electrical and Computer Engineering, National University of Singapore.
c Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China.
d Fujian Metal-New Aluminum Technology Co., Ltd.

Overview:

This repository implements an innovative causal-aware spatiotemporal network designed for interpretable anomaly monitoring and root-cause analysis in complex industrial processes. By integrating a prediction-reconstruction framework, sequential causal graph inference, time-frequency feature fusion, and graph attention mechanisms, it effectively captures intricate spatiotemporal relationships among variables, enabling interpretable anomaly detection.