Work

DG-FuseNet: A Dual-Scale Dynamical Gated Fusion Framework for Cross-Domain Fault Diagnosis in Rotating Machinery

Year: 2026

Type: article

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.

Source: Sensors

DOI: 10.3390/s26102968

Authors: Dawei Zhang, Xiaoheng Deng, Yun Liao, Jinsong Gui, Jingjing Zhang

Institution: Central South University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137