多度量下ResGAT的风力发电机齿轮箱故障诊断

李明; 曹洁; 刘宗礼; 王进花

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

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (6) : 683-690. DOI: 10.19912/j.0254-0096.tynxb.2024-0173

多度量下ResGAT的风力发电机齿轮箱故障诊断

李明¹, 曹洁, 刘宗礼¹, 王进花²

作者信息 +

GEARBOX FAULT DIAGNOSIS WITH RESGAT UNDER MULTIPLE DISTANCE METRICS

Li Ming¹, Cao Jie, Liu Zongli¹, Wang Jinhua²

Author information +

文章历史 +

摘要

针对现有深度学习方法在风力发电机齿轮箱故障诊断方面的特征提取和样本相似性建模局限性,提出一种多种距离度量下残差连接的图注意力网络（ResGAT）。该方法构建全连接图以生成邻接矩阵,并结合多种距离度量方法,充分挖掘样本之间的相似性。利用图注意力网络进行节点特征聚合,结合残差连接以减轻模型梯度消失风险。进一步地,在Adam优化器中融入L2正则化及偏置校正,以降低过拟合问题。实验结果显示,ResGAT方法在WT-Planetary gearbox dataset齿轮箱数据集上能有效提取样本间相似性,并在风力发电机齿轮箱故障诊断上展现出优异性能。

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.

导出引用

李明, 曹洁, 刘宗礼, 王进花. 多度量下ResGAT的风力发电机齿轮箱故障诊断[J]. 太阳能学报. 2025, 46(6): 683-690 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0173

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

中图分类号： TP277 TH133.33

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