Topology-Guided Graph Learning for Process Fault Diagnosis

Abstract

Faults in the process industry can be diagnosed using various data-driven methods, but the intrinsic relationships between inputs and outputs, particularly the physical consistency of model prediction logic, have received little attention. To address this issue, we propose a topology-guided graph learning fault diagnosis framework that combines the concept of graphs with process physics. Our framework focuses on knowledge embedding and explanation and includes several key components: a topology graph based on the flowchart, a self-attention mechanism to discover distinctive knowledge from data, graph convolution to capture variable relationships, graph pooling to coarsen graph data, and a gating mechanism to establish long-term dependencies. We also use a graph explainer to assess the physical consistency of the model’s prediction logic. We demonstrate the feasibility of our method using the Tennessee Eastman process and show that it is not a black-box model but rather has natural advantages in terms of effectiveness and explanation.