基于深度卷积条件生成对抗网络的风电机组轴承故障诊断方法

王娜; 王子从; 刘佳林

doi:doi:10.19912/j.0254-0096.tynxb.2024-2045

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基于深度卷积条件生成对抗网络的风电机组轴承故障诊断方法

更新时间：2026-04-08

- 基于深度卷积条件生成对抗网络的风电机组轴承故障诊断方法
- Vol. 47, Issue 3, Pages: 402-411(2026)
- 作者机构：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-2045
  CLC： TH133
- Online First：07 April 2026，
  
  Published：2026
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王娜, 王子从, 刘佳林. 基于深度卷积条件生成对抗网络的风电机组轴承故障诊断方法[J]. 2026, 47(3): 402-411.
DOI：

王娜, 王子从, 刘佳林. 基于深度卷积条件生成对抗网络的风电机组轴承故障诊断方法[J]. 2026, 47(3): 402-411. DOI： doi:10.19912/j.0254-0096.tynxb.2024-2045.

摘要

针对风电机组内部滚动轴承的故障样本不足而易导致故障诊断精度下降的问题

提出一种基于深度卷积条件生成对抗网络（DC-CWGAN）的诊断方法。首先对振动信号进行连续小波变换（CWT）

构建对应的时频特征图集

以增强模型的故障特征捕捉能力;其次使用卷积结构替代条件生成对抗网络（CGAN）中的全连接层

并引入Wasserstein距离重构CGAN的损失函数

以提升DC-CWGAN中生成样本的质量

并提高网络训练过程中的稳定性;然后通过模型迁移策略的应用提高目标分类网络的泛化能力和计算效率。实验证明

所提方法能有效提高小样本问题下的轴承诊断准确率。

Abstract

For the accuracy decreasing in fault diagnosis derived from the samples insufficient of the rolling bearings in wind turbines

a fault diagnosis method based on deep convolutional-conditional Wasserstein generative adversarial network （DC-CWGAN） is proposed. Firstly

the continuous wavelet transform （CWT） is applied to the vibration signals to construct a dataset of time-frequency feature images. As a result

the feature capture ability for the fault model is increased. Secondly

the fully connected layers in the conditional generative adversarial networks （CGAN） are replaced by the convolution al structures. Followingly

the Wasserstein distance is introduced to reconstruct the CGAN loss function. Thus the quality of the generated samples and the stability of the DC-CWGAN training are both strengthened. Thirdly

with the application of the model transfer strategy

the generalization and the computational efficiency of the objective classification network are enhanced. Finally

it is demonstrated that the diagnosis accuracy of the rolling bearings under the small-sample condition is improved effectively by the proposed method.

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references

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