基于EMD-GAF和改进的SERE-DenseNet的滚动轴承故障诊断方法

为了解决滚动轴承一维振动信号中故障特征微弱难以提取和深度学习模型层数加深容易导致梯度消失或梯度爆炸从而引起模型恶化、导致故障诊断准确率低和鲁棒性差的问题，本文提出一种基于EMD-GAF和改进的SERE-DenseNet的滚动轴承故障诊断方法。将滚动轴承一维振动信号通过滚动采样后利用EMD对其进行分解并重构，再使用GAF将重构的一维信号转换为二维图像作为模型输入，模型方面选取DenseNet121为主干，引入了SERE模块，并将2层卷积的Dense Layer改进为3层稀疏的、基数为8的模块；将二维图像作为输入通过该模型进行特征提取和故障分类。采用凯斯西储大学的轴承数据集进行仿真实验，实验结果表明，本文方法能够准确地完成滚动轴承故障诊断，故障诊断最大准确率100%,10次实验平均准确率99.91%,与常见的深度学习模型进行比较，本文方法具有较大的优越性；在信噪比为10 dB的环境下故障诊断准确率为96.48%,本文方法具有较强的鲁棒性。

Fault diagnosis method of rolling bearing based on EMD-GAF and improved SERE-DenseNet

In order to solve the problem of weak fault feature extraction in one-dimensional vibration signal of rolling bearing; In order to solve the problem that the deepening of deep learning model layer is easy to lead to the disappearance of gradient or the deterioration of gradient explosion, which leads to the low accuracy and poor robustness of fault diagnosis, this paper proposes a rolling bearing fault diagnosis method based on EMD-GAF and improved SERE-DenseNet. One-dimensional vibration signals of rolling bearings were decomposed and reconstructed by EMD after rolling sampling, and the reconstructed one-dimensional signals were converted into two-dimensional images by GAF as model input. DenseNet121 was selected as the main task in terms of model, and SERE module was introduced. The Dense Layer with 2 convolution layers is improved into 3 sparse modules with base number of 8. Feature extraction and fault classification are carried out by using 2D image as input. The bearing data set of Case Western Reserve University was used for simulation experiments. The experimental results show that the proposed method can accurately diagnose rolling bearings, with the maximum accuracy of 100% and the average accuracy of 99.91% in 10 experiments. Compared with the common deep learning model, the proposed method has great advantages. The fault diagnosis accuracy is 96.48% when the signal to noise ratio is 10 dB, and the proposed method has strong robustness.