@article{Hao2026, 
author = {Fang Hao and Puyuan Hu and Yumo Jiang and Ruonan Liu},
title = {Graph Representation Consistency Enhancement via Graph Transformer for Fault Diagnosis of Complex Industrial Systems},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {87},
number = {2},
pages = {63},
keywords = {Graph neural networks, graph transformer, consistency loss},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.075655},
doi = {10.32604/cmc.2026.075655},
abstract = {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.}
}