MS-DDGformer: A multi-scale synergized dual-driven graph transformer for industrial process fault diagnosis.

Authors

Dazi Li, Xiyao Zhu, Yurui Zhu, Hamid Reza Karimi

Published in

ISA transactions. May 19, 2026. Epub May 19, 2026.

Abstract

Fault diagnosis in modern industrial systems is challenged by complex failures that require sophisticated spatio-temporal modeling. Although Graph Transformers (GTs) show promise, existing models often lack effective architectural coupling or are tuned for non-industrial tasks. They struggle with nonstationary process dynamics and fail to jointly model temporal dependencies and static topology. To overcome these issues, a Multi-Scale Synergized Dual-Driven Graph Transformer (MS-DDGformer) is proposed. Its core is a dual-driven backbone network that deeply fuses structural and temporal information through alternating G-Blocks and T-Blocks. The backbone is complemented by a parallel synergistic spatio-temporal branch comprising (i) a temporal-driven branch with an attention-guided edge adaptation mechanism to promote information flow and (ii) a structure-driven branch that preserves important structural features to avoid over-globalization. A multi-scale fusion module performs weighted integration of the backbone and branch outputs to enable a comprehensive representation. Moreover, a graph construction method combining k-nearest neighbors (KNN) with random walk encoding is designed to better model latent variable relationships. Experiments on three industrial cases validate that MS-DDGformer achieves superior performance over competing GNN-based, temporal, and GT models.

PMID:
42697799
Bibliographic data and abstract were imported from PubMed on 05 Sep 2026.

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