基于DFT与ECA的滚动轴承故障诊断
Fault Diagnosis of Rolling Bearing Based on Discrete Fourier Transform and Efficient Channel Attention
张顺 1邓艾东 1徐硕 1丁雪1
作者信息
- 1. 东南大学大型发电装备安全运行与智能测控国家工程研究中心 南京,210096
- 折叠
摘要
针对滚动轴承故障诊断中传统卷积神经网络(convolutional neural networks,简称CNN)提取特征的感受野受限于卷积核大小的问题,提出了一种结合离散傅里叶变换(discrete Fourier transform,简称DFT)和高效通道注意力(efficient channel attention,简称 ECA)的卷积神经网络模型(convolutional neural network combining discrete Fourier transform and efficient channel attention,简称DFT-ECANet).首先,将原始振动信号通过DFT变换到频域,在频域上经卷积和离散傅里叶逆变换(inverse discrete Fourier transform,简称IDFT)转换到时域,使信号在时域上具有全局的感受野;其次,将该信号与经过卷积的数据在通道维度上进行拼接,通过ECA为各通道数据分配权重,并关注诊断性能高的特征;最后,通过多个卷积-池化层进一步提取模型深层特征,结合池化层和全连接层诊断轴承故障.实验结果表明:DFT-ECANet在原始振动数据集上具有较高的诊断精度和较好的泛化性能,通过T分布随机近邻嵌入(T-distributed stochastic neighbor embedding,简称T-SNE)可降维可视化模型的诊断过程;在强噪声干扰下仍能保持较高的精度,具备较强的鲁棒性和抗噪性能.
Abstract
Aiming at the problem that the feature receptive field extracted by traditional convolutional neural network(CNN)in rolling bearing fault diagnosis is limited by the shape of convolution kernel,a network(DFT-ECANet)combining discrete Fourier transform(DFT)and efficient channel attention(ECA)is pro-posed.Firstly,convert the original vibration signal into the frequency domain through DFT,and transform it into the time domain through convolution in the frequency domain and make the signal has a global receptive field in the time domain through inverse discrete Fourier transform(IDFT);then,concatenate the signal with the convoluted data on the channel dimension,assign weight to each channel data through ECA,focusing on features with high diagnostic performance;finally,the deep features of the model are further extracted through several convolution-pooling pairs,and the fault diagnosis of rolling bearing is performed by linking the pooling layers and the fully connected layer.The experimental results show that DFT-ECANet has high diagnostic accu-racy and good generalization performance on the original vibration datasets,and the diagnostic process of the model is visualized through T-SNE dimensionality reduction;it can still maintains high accuracy,robustness and anti-noise property under fierce noise interference.
关键词
滚动轴承/故障诊断/卷积神经网络/离散傅里叶变换/高效通道注意力Key words
rolling bearing/fault diagnosis/convolutional neural network/discrete Fourier transform/efficient channel attention引用本文复制引用
基金项目
国家自然科学基金(51875100)
江苏省重点研发计划(BE2020034)
出版年
2024
NETL
NSTL
万方数据