双通道输入LetNet-5卷积神经网络旋转机械故障诊断模型研究

Research on fault diagnosis model of rotating machinery based on two-channel input LetNet-5 convolution neural network

针对旋转机械运行过程中伴随着诸多噪声,现有单通道网络在旋转机械故障诊断过程中抗噪性较差的问题,提出了一种加入并联机制的双通道输入Let Net-5卷积神经网络模型。模型合理性检验过程采用了凯斯西储大学轴承数据集,在此基础上,添加信噪比为–10 d B的高斯白噪声模拟真实噪声情形;采用短时傅里叶变换将电机风扇端和驱动端振动数据进行处理,获得的时频图像传递至双通道输入的Let Net-5卷积神经网络进行训练学习。研究结果表明:双通道输入Let Net-5卷积神经网络模型能够良好捕捉到强噪声环境下的故障特征;相比于多尺度特征融合残差模型、多模态耦合输入神经网络模型、传统的K近邻与决策树模型及单通道输入Let Net-5卷积神经网络模型,双通道输入Let Net-5卷积神经网络具有更高的效率和精度。

Existing single-channel networks have poor noise immunity during fault diagnosis of rotating machinery due to the many noises associated with the operation of rotating machinery.To address this problem,a two-channel input LetNet-5 convolutional neural network model incorporating a parallel mechanism was proposed.Case Western Reserve University bearing dataset was used for the model plausibility check process,based on which Gaussian white noise with a signal-to-noise ratio of-10 dB was added to simulate the real noise situation.The shorttime Fourier transform was used to process the motor fan-side and drive-side vibration data,and the resulting timefrequency images were passed to a two-channel input LetNet-5 convolutional neural network for training and learning.The results show that,the dual-channel input LetNet-5 convolutional neural network model is able to capture the fault features in a strong noise environment well,it has higher efficiency and accuracy than the multiscale feature fusion residual model,the multimodal coupled input neural network model,the conventional K-nearest neighbour and decision tree model and the single-channel input LetNet-5 convolutional neural network model.