Causal temporal graph attention network for fault diagnosis of chemical processes

Fault detection and diagnosis (FDD) plays a significant role in ensuring the safety and stability of chemical processes. With the development of artificial intelligence (AI) and big data technologies, data-driven approaches with excellent performance are widely used for FDD in chemical processes. However, the improved predictive accuracy has often been achieved through increased model complexity, which turns models into black-box methods and causes the uncertainty regarding their decisions. In this study, a causal temporal graph attention network (CTGAN) is proposed for fault diagnosis of chemical processes. A chemical causal graph is built by causal inference to represent the propagation path of faults. Attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations. Experiments in the Tennessee Eastman (TE) process and the Green Ammonia (GA) process showed that CTGAN achieved high performance and good explainability.