基于SE-ResNeXt的滚动轴承故障诊断方法

针对滚动轴承在故障诊断过程中信号特征提取困难导致诊断准确率低、鲁棒性差的问题,提出一种基于Squeeze-Excitation-ResNeXt(SE-ResNeXt)网络的滚动轴承故障诊断方法;将采集的一维轴承振动信号作为输入,进行滑动窗口采样与标准化处理,通过压缩、激励操作进行特征重标定,扩大模型感受野,并级联聚集残差变换网络自适应提取故障信号特征;在模型训练过程中选择最优压缩率为1/8以及8个组卷积,引入Relu函数加快网络收敛,使用全局平均池化替代全连接层避免过拟合现象,构造能够自主进行表征学习的最优故障诊断模型;通过仿真实验表明:与目前的深度学习算法相比,SE-ResNeXt网络能够准确的实现轴承故障诊断,并在高噪声的环境下仍具有较好的鲁棒性。

Fault Diagnosis Method of Rolling Bearing Based on SE-ResNeXt

For the problem of low diagnostic accuracy and robustness due to the difficulty in extracting the signal features of rolling bearings during fault diagnosis, a new rolling bearing fault diagnosis method is proposed based on Squeeze-Excitation-ResNeXt(SE-ResNeXt). The collected one-dimensional bearing vibration signals were taken as input, the sliding window sampling and standardization were conducted, the feature re-calibration was carried out through compression and excitation operation, the model receptive field was enlarged and the fault signal characteristics were extracted adaptively by cascading aggregate residual transformation network. In the process of model training, the optimal compression rate was selected as 1/8 and 8 sets of convolution, Relu function was introduced to accelerate the convergence of the network, global average pooling was used to replace the full connection layer to avoid overfitting, and an optimal fault diagnosis model capable of independent characterization learning was constructed. Simulation experiments show that compared with the current deep learning algorithm, the SE-ResNeXt network can accurately realize bearing fault diagnosis and still has good robustness under high noise environment.