ArticleSensors (Basel, Switzerland)2026
DG-FuseNet: A Dual-Scale Dynamical Gated Fusion Framework for Cross-Domain Fault Diagnosis in Rotating Machinery.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
To address the challenges of insufficient scale normalization, limited time-frequency localization, and ineffective multi-scale feature extraction in the intelligent fault diagnosis of rotating components under varying operating conditions, we propose a novel convolutional neural network, termed DG-FuseNet. The proposed method was validated on real-world datasets from train vehicle vibration signals and aero-engine systems, achieving diagnostic accuracies of 99.76% and 94.32%, respectively. Compared with eleven advanced intelligent models, DG-FuseNet demonstrated faster convergence, higher diagnostic accuracy, strong robustness against interference, and superior generalization capability. These results indicate that DG-FuseNet outperforms existing approaches in complex industrial scenarios, highlighting its excellent performance and stability.
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