基于WLT-GAN的轴承不平衡数据故障诊断方法

焦华超; 孙文磊; 王宏伟; 万晓静

doi:10.19912/j.0254-0096.tynxb.2024-2035

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (3) : 392-401. DOI: 10.19912/j.0254-0096.tynxb.2024-2035

基于WLT-GAN的轴承不平衡数据故障诊断方法

焦华超, 孙文磊, 王宏伟, 万晓静

作者信息 +

BEARING FAULT DIAGNOSIS METHOD FOR IMBALANCED DATA USING WLT-GAN

Jiao Huachao, Sun Wenlei, Wang Hongwei, Wan Xiaojing

Author information +

文章历史 +

摘要

针对因轴承故障数据不平衡导致故障诊断模型准确率下降的问题,提出一种基于类小波变换生成对抗网络（WLT-GAN）的故障诊断方法。该方法将类小波变换神经网络嵌入生成器,并结合双判别器架构,使WLT-GAN能够深度学习信号的时域和频域特征,生成高质量的故障数据,从而有效缓解数据不平衡问题。此外,还引入集成学习构建故障诊断模型,通过软投票机制融合多源特征提高诊断精度。实验结果表明,WLT-GAN生成的样本在时域和频域特征分布上与真实数据高度相似,且该模型凭借集成学习优势,展现出较高的准确性与鲁棒性,可为风电机组轴承故障诊断提供高效、可靠的解决方案。

Abstract

To address the degradation in fault diagnosis accuracy caused by the imbalance of bearing fault data, this paper proposes a fault diagnosis method based on a wavelet-like transform generative adversarial network （WLT-GAN）. In the proposed method, the wavelet-like transform neural network is embedded into the generator and combined with a dual-discriminator architecture, enabling the WLT-GAN to jointly learn time-domain and frequency-domain features from vibration signals and generate high-quality fault samples to effectively alleviate data imbalance. In addition, an ensemble learning strategy is employed to construct the fault diagnosis model, where a soft-voting mechanism integrates multi-source features to improve diagnostic accuracy. Experimental results demonstrate that the samples generated by WLT-GAN exhibit high similarity to real data in both time- and frequency-domain feature distributions. Leveraging the advantages of ensemble learning, the proposed method achieves high accuracy and robustness, providing an efficient and reliable solution for bearing fault diagnosis in wind turbine generators.

导出引用

焦华超, 孙文磊, 王宏伟, 万晓静. 基于WLT-GAN的轴承不平衡数据故障诊断方法[J]. 太阳能学报. 2026, 47(3): 392-401 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2035

Jiao Huachao, Sun Wenlei, Wang Hongwei, Wan Xiaojing. BEARING FAULT DIAGNOSIS METHOD FOR IMBALANCED DATA USING WLT-GAN[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 392-401 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2035

中图分类号： TP181 TH133.3

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