针对风电机组轴承故障样本少且诊断准确率低的问题,提出一种基于数字孪生的风电机组轴承故障诊断方法。构建了风电机组的数字孪生系统,为风电机组轴承故障诊断提供了数据来源。基于希尔伯特黄变换对轴承振动信号增强处理,实现了振动信号样本数据的增强,减少振动信号噪音。建立了风电机组轴承故障的卷积神经网络模型,以数据增强的样本作为诊断模型的训练与测试。实验验证了所提方法的可行性与有效性,解决了一维振动信号的数据增强,提高了风电轴承故障诊断准确率与稳定性。

Aiming at the issue of less fault samples and low diagnosis accuracy for wind turbine bearing, an approach for fault diagnosis of wind turbine bearing based on digital twin is proposed. First, digital twin system framework for wind turbine is constructed, which can provide data support for bearing fault diagnosis. Then, vibration signal of bearing is processed based on HHT (Hilbert-Huang transform) for data augmentation of sample data and noise reduction of vibration signal. Subsequently, wind turbine bearing fault diagnosis model is established based on CNN (convolutional neural network), where data augmentation sample is used as training and testing sample. Finally, experiment is designed to verify the feasibility and effectiveness of the proposed approach, which can realize the data enhancement of one-dimensional vibration signal and improve accuracy and stability of fault diagnosis of wind turbine bearing.