基于穿越可视图和图同构网络的风电传动系统故障诊断方法

周忠志; 邓艾东; 刘东瀛; 刘洋; 胡沁怡; 饶朗

doi:10.19912/j.0254-0096.tynxb.2023-1670

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (2) : 591-599. DOI: 10.19912/j.0254-0096.tynxb.2023-1670

基于穿越可视图和图同构网络的风电传动系统故障诊断方法

周忠志^1,2, 邓艾东^1,2, 刘东瀛³, 刘洋^1,2, 胡沁怡^1,2, 饶朗^1,2

作者信息 +

FAULT DIAGNOSIS METHOD FOR WIND POWER TRANSMISSION SYSTEM BASED ON PENETRABLE VISIBILITY GRAPH AND GRAPH ISOMORPHISM NETWORKS

Zhou Zhongzhi^1,2, Deng Aidong^1,2, Liu Dongying³, Liu Yang^1,2, Hu Qinyi^1,2, Rao Lang^1,2

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文章历史 +

摘要

为解决实际工程应用中风电机组传动系统在小样本情况下的故障诊断困难问题,提出一种基于穿越可视图和图同构网络的旋转机械故障诊断方法。首先,利用有限穿越可视图算法将时间序列信号数据转换为图结构数据,并对连接边进行加权;然后,将加权图数据输入到网络模型中进行训练,模型引入自注意力机制以实现适应性建模并提高模型的泛化能力;最后,利用Softmax分类器实现故障诊断任务。实验结果表明所提方法能在极限小样本数量下取得较好的故障诊断效果。

Abstract

To address the difficulty in fault diagnosis of wind turbine transmission systems with limited samples, a novel fault diagnosis approach for rotating machinery is proposed, which leverages the Penetrable Visibility Graph and Graph Isomorphism Network. Initially, the time series signal data is transformed into graph-structured data using the penetrable visibility graph algorithm with limited depth, and the resulting connecting edges are then weighted. Subsequently, the weighted graph data is fed into a network model for training. Specifically, the model integrates a self-attention mechanism to enable adaptive modeling, thereby improving its generalization ability. Finally, a Softmax classifier is employed for fault pattern recognition. Experimental results demonstrate the effectiveness of the proposed method in achieving robust fault diagnosis performance, especially in scenarios with an extremely limited number of samples.

导出引用

周忠志, 邓艾东, 刘东瀛, 刘洋, 胡沁怡, 饶朗. 基于穿越可视图和图同构网络的风电传动系统故障诊断方法[J]. 太阳能学报. 2025, 46(2): 591-599 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1670

Zhou Zhongzhi, Deng Aidong, Liu Dongying, Liu Yang, Hu Qinyi, Rao Lang. FAULT DIAGNOSIS METHOD FOR WIND POWER TRANSMISSION SYSTEM BASED ON PENETRABLE VISIBILITY GRAPH AND GRAPH ISOMORPHISM NETWORKS[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 591-599 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1670

中图分类号： TH133.33

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