基于改进降噪自编码器的风机轴承故障诊断方法

Wind turbine bearing fault diagnosis method based on an improved denoising AutoEncoder

滚动轴承是风电机组中故障最为频繁的部件之一,准确有效的轴承故障诊断方法有助于保障风电机组安全稳定运行。针对轴承振动信号特征微弱、难以诊断的问题,提出了一种基于改进降噪自编码器的风电机组轴承故障检测方法。首先引入了一维信号的图像化预处理,将原始的时域信号转化为二维特征灰度图。然后利用卷积神经网络在图像特征提取上的强大优势,构建了堆叠降噪自编码器与卷积神经网络的集成模型,去除了传统卷积神经网络中的池化层,进一步提升提取特征的鲁棒性和泛化性。整体诊断流程由数据驱动,减少了对于经验的依赖。最后的实验结果表明,该方法能够精确诊断不同类型的轴承故障。此外,通过与其他方法的对比实验进一步验证了该方法在故障诊断方面的优越性。

The rolling bearing is one of the most frequently faulty components in wind turbines.Accurate and effective bearing fault diagnosis methods can help ensure safe and stable operation.Bearing vibration signal characteristics are weak and difficult to diagnose,so a fault diagnosis method based on an improved denoising AutoEncoder is proposed.First,a one-dimensional signal imaging method to convert the original time domain signal into a two-dimensional feature grayscale image is introduced.Secondly,using the advantage of a convolutional neural network in image feature extraction,a combination model based on a stacked denoising AutoEncoder and convolutional neural network is proposed.The pooling layer in the traditional convolutional neural network is removed to ensure the robustness and generalization of extracted features.The overall diagnosis process is driven by data,reducing reliance on expert experience.Lastly,experimental results show that this method can accurately diagnose different types of bearing faults.Comparison experiments with other methods further verify the superiority of this method in fault diagnosis。