Abstract
Bearings are one of the critical components in rotating machinery. Bearing failures can lead to equipment damage, reduced performance, and even major safety accidents. Therefore, improving the ability to diagnose bearing faults can help improve the availability, reliability, and safety of rotating machinery. However, the original vibration signals in rotating machinery often contain noise and irregularities, making it difficult for traditional vibration analysis to extract effective high-dimensional features. Inspired by the construction of spatio-temporal graphs and dual-branch graph networks, a bearing fault diagnosis method based on dual-branch spatio-temporal graph networks (DBSGN) is proposed. Firstly, the vibration signal is modeled based on spectrum theory and the spectrum analysis method to construct a spatio-temporal graph. Secondly, Laplace-based spectral decomposition is used to extract the feature vectors of samples in the spatio-temporal graph. Finally, we designed a dual-branch fusion network to train and verify the bearing data and adjusted the model’s learning of the bearing data through a dynamic attention mechanism. The experimental results on three benchmark datasets indicate that DBSGN outperforms traditional models in terms of stability and accuracy.
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Data availability
The datasets generated or analysed during the current study are not publicly available due [Protect the intellectual property rights and commercial interests of investors] but are available from the corresponding author on reasonable request.
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Funding
This study supported by CHN Shenhua Energy Co.Ltd. Shendong Coal Branch No: E210100625.
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LI Yang conducted literature research; WANG Yajun and LI Yang provided the subject source; WANG Yajun provided financial support; WANG Yajun LI Yang designed the algorithm; DAI Qi were responsible for the experimental design; LI Chenggang implement the algorithm in code; DAI Qi have sorted out the data; LI Yang wrote the main manuscript text; All authors reviewed the manuscript.
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Wang, Y., Li, Y., Li, C. et al. Dual-branch spatio-temporal graph network for bearing fault diagnosis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42504-0
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DOI: https://doi.org/10.1038/s41598-026-42504-0
