A Knowledge-Data Integrated Graph Convolutional Network for Fault Diagnosis in Industrial Processes

Modern industrial processes are rapidly evolving toward intelligent operation, creating new challenges for fault diagnosis in complex systems. This article presents a knowledge-data integrated graph convolutional network (KDIGCN) that combines domain knowledge with data-driven strategies. The method partitions process variables into subgraphs based on physical mechanisms and uses temporal convolutional networks (TCNs) to construct causal adjacency matrices, effectively integrating prior knowledge with temporal dependencies. An abnormal feature enhancement mechanism improves sensitivity to fault indicators, while multiscale convolutional neural networks (MS-CNNs) enable spatiotemporal feature fusion across different time scales and subgraphs. Extensive experiments on the Tennessee Eastman (TE) benchmark demonstrate that KDIGCN achieves superior diagnostic accuracy and robustness compared to state-of-the-art methods, particularly for similar faults and unknown fault scenarios.