ProTopormer: Toward Understandable Fault Diagnosis Combining Process Topology for Chemical Processes

Abstract

In modern chemical processes, fault detection and diagnosis (FDD) is a key part of abnormal situation management (ASM). As researchers continue to improve the fault diagnosis performance of different models, the emphasis on model interpretability and explainability studies has increased in recent years. In this paper, a novel model, ProTopormer, was proposed for fault diagnosis of chemical processes. Self-attention mechanism and process topology knowledge were fully combined to achieve high model performance and good interpretability. Experiments on Tennessee Eastman process showed that the model achieved a high diagnosis rate and a low false alarm rate. Attention weights were visualized and quantitatively analyzed to identify key variables for fault diagnosis, which showed strong explanations for the root causes of different faults.