Dual-branch fusion framework with graph attention networks for compound fault diagnosis in complex machinery system

Mubarak, Akram and Gebremariam, Mebrahitom and Isa, Hilmi and Azhari, Azmir (2026) Dual-branch fusion framework with graph attention networks for compound fault diagnosis in complex machinery system. Scientific Reports. ISSN 2045-2322

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Abstract

The present research proposes a generalized diagnostic framework for complex machinery that integrates domain knowledge with learned signal representations while exploiting the physical coupling between subsystems. By modelling multi-component systems as heterogeneous graphs, the framework employs a dual-branch architecture: Branch A extracts handcrafted features grounded in failure mechanics, while Branch B learns complementary latent embeddings via an unsupervised pre-trained 1-D convolutional autoencoder. To prevent geometric imbalance and ensure both branches contribute equally to diagnostic performance, Normalized Principal Component Analysis (nPCA) equalizes the feature spaces before a two-layer Graph Attention Network (GAT) performs topology-aware spatial fusion. Final health states are classified using a Platt-calibrated Support Vector Machine (SVM) with a One-Class extension, producing probabilistic labels alongside a risk index and a normalized entropy uncertainty index for robust open-set rejection. Validated on the PHM-Beijing 2024 Final Stage subway bogie drivetrain benchmark, the pipeline achieves 95.1%, 87.1%, and 82.4% accuracy across single-component, component-level compound, and system-level compound fault tiers, respectively. Crucially, the topology-aware GAT spatial fusion provides a unique + 12.4% point accuracy margin exclusively on system-level compound faults, successfully decoupling highly non-additive joint fault signatures where traditional independent ensemble methods collapse. Results confirm that the learned GAT attention weights align with physical coupling pathways such as the high-weight Motor-Gearbox connection and the uncertainty quantification effectively identifies 181 fault-affected samples from 252 unlabelled test cases under deliberate domain shift. Furthermore, cross-dataset verification conducted on the public Case Western Reserve University (CWRU) and University of Ottawa benchmarks demonstrates a superior 96.39% mean precision under cross-load transfer alongside highly stable uncertainty indicators under severe non-stationary speed dynamics. Operating under a severe dataset shift, the Platt-calibrated safety gate outputs a 86.5% low-confidence (LOW_CONF) triage flag distribution. This mechanism explicitly converts epistemic uncertainty into reliable human-in-the-loop work orders rather than issuing overconfident incorrect predictions, demonstrating significant practical engineering value for safety-critical industrial assets. This framework provides a monotonic severity profile and a reliable rejection mechanism for out-of-distribution conditions, serving as a blueprint for deployable diagnostic systems in multi-component rotating machinery.

Item Type:	Article
Identification Number:	10.1038/s41598-026-62410-9
Dates:	Date Event 10 July 2026 Accepted 16 July 2026 Published Online
Uncontrolled Keywords:	Condition-based maintenance (CBM), Compound fault diagnosis, Graph Attention Network (GAT), Multi-component rotating machinery, Dual-branch feature fusion, Uncertainty Quantification (UQ).
Subjects:	CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-01 - engineering (non-specific)
Divisions:	Architecture, Built Environment, Computing and Engineering > Engineering
Depositing User:	Gemma Tonks
Date Deposited:	26 Aug 2026 10:09
Last Modified:	26 Aug 2026 10:09
URI:	https://www.open-access.bcu.ac.uk/id/eprint/17192

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