DSTN: Dynamic Spatio-Temporal Network for Early Fault Warning in Chemical Processes

Chenming Duan, Zhichao Wu, Li Zhu, Xirong Xu, Jianmin Zhu, Ziqi Wei, Xin Yang

Abstract: Highlights•One-dimension convolution enhances Transformer’s ability to learn global local feature weights.•The K-order adjacency matrix and sliding window are used to construct higher-order dynamic graphs.•The residual connects spatial and temporal features to improve the learning ability of the model.•The effectiveness and practicability of method are verified by two chemical process datasets.