GEARBOX FAULT DIAGNOSIS WITH RESGAT UNDER MULTIPLE DISTANCE METRICS

Li Ming; Cao Jie; Liu Zongli; Wang Jinhua

doi:10.19912/j.0254-0096.tynxb.2024-0173

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (6) : 683-690. DOI: 10.19912/j.0254-0096.tynxb.2024-0173

GEARBOX FAULT DIAGNOSIS WITH RESGAT UNDER MULTIPLE DISTANCE METRICS

Li Ming¹, Cao Jie, Liu Zongli¹, Wang Jinhua²

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Abstract

To address the limitations of existing deep learning methods in feature extraction and sample similarity modeling for wind turbine gearbox fault diagnosis, we propose a Residual Connected Graph Attention Network （ResGAT） that incorporates multiple distance metrics. This approach constructs a fully connected graph to generate an adjacency matrix and integrates various distance metric methods to fully explore the similarity between samples. Utilizing graph attention networks for node feature aggregation, coupled with residual connections, mitigates the risk of gradient vanishing. Furthermore, L2 regularization and bias correction are incorporated into the Adam optimizer to mitigate overfitting issues. Experimental results demonstrate that the ResGAT method effectively captures sample similarity on the WT-Planetary gearbox dataset and exhibits outstanding performance in fault diagnosis.

Key words

wind turbine / gearbox / fault diagnosis / deep learning / graph attention network / overfitting

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Li Ming, Cao Jie, Liu Zongli, Wang Jinhua. GEARBOX FAULT DIAGNOSIS WITH RESGAT UNDER MULTIPLE DISTANCE METRICS[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 683-690 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0173

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