@Article{cmc.2026.075655,
AUTHOR = {Fang Hao, Puyuan Hu, Yumo Jiang, Ruonan Liu},
TITLE = {Graph Representation Consistency Enhancement via Graph Transformer for Fault Diagnosis of Complex Industrial Systems},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {87},
YEAR = {2026},
NUMBER = {2},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v87n2/66629},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2026.075655}
}