TY  - EJOU
AU  - Hao, Fang 
AU  - Hu, Puyuan 
AU  - Jiang, Yumo 
AU  - Liu, Ruonan 

TI  - Graph Representation Consistency Enhancement via Graph Transformer for Fault Diagnosis of Complex Industrial Systems
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 87
IS  - 2
SN  - 1546-2226

AB  - Industrial fault diagnosis is a critical challenge in complex systems, where sensor data is often noisy and interdependencies between components are difficult to capture. Traditional methods struggle to effectively model these complexities. This paper presents a novel approach by transforming fault diagnosis into a graph recognition task, using sensor data represented as graph-structured data with the k-nearest neighbors (KNN) algorithm. A Graph Transformer is applied to extract node and graph features, with a combined loss function of cross-entropy and weighted consistency loss to stabilize graph representations. Experiments on the TFF dataset show that Graph Transformer combined with consistency loss outperforms conventional methods in fault diagnosis accuracy, offering a promising solution for enhancing fault detection in industrial systems.
KW  - Graph neural networks; graph transformer; consistency loss

DO  - 10.32604/cmc.2026.075655