利用改进卷积神经网络的滚动轴承变工况故障诊断方法

Improved CNN-Based Fault Diagnosis Method for Rolling Bearings under Variable Working Conditions

针对滚动轴承在强噪声环境和变工况下故障诊断效果不佳、泛化能力差的问题,提出一种改进卷积神经网络(CNN)的滚动轴承变工况故障诊断方法。设计了多尺度特征提取模块,采用不同尺度的卷积层提取对输入数据特征,实现了提取故障数据中特征信息最大化。同时,引入通道注意力机制,提取出该模块中更重要、更关键的信息;设计了带跳跃连接线的卷积模块,防止提取到的丰富特征在卷积层前向传递时丢失;以Softmax交叉熵作为损失函数,利用Adam优化算法实现滚动轴承故障诊断。将所提方法分别在凯斯西储大学轴承数据集和变速箱数据集上进行实验验证,结果表明:在凯斯西储大学轴承数据集上的变噪声实验中,所提方法诊断平均准确率为96.49%,在变工况中诊断准确率在90%以上,均高于比较方法;在变速箱轴承数据集上,所提方法诊断准确率为99.54%,具有较好的抗噪性和更好的泛化能力。

Aiming at the worse fault diagnosis of rolling bearing and poor generalization ability in a strong noise environment and variable working conditions,an improved CNN-based fault diagnosis method for rolling bearing under variable working conditions is proposed.A multi-scale feature extraction module is designed,and convolutional layers of different scales are adopted to extract features from the input data to maximize the extraction of feature information in the fault data.The channel attention mechanism is then introduced to extract the more important and critical components from this module.A convolution module with skip connection lines is designed to prevent the extracted rich features from being lost when the convolutional layer is forwarded.Regarding softmax cross entropy as the loss function,the Adam optimization algorithm is chosen to realize the fault diagnosis for rolling bearing.The proposed method is verified by experiments on the bearing dataset and gearbox dataset from Case Western Reserve University.The results show that in the variable noise experiment on the bearing dataset from Case Western Reserve University,the proposed method achieves an average diagnostic accuracy rate of 96.49%,and the diagnostic accuracy rate is beyond 90%in variable working conditions,which are obviously higher than the competing methods.On the gearbox bearing data set,the diagnostic accuracy rate of the proposed method with better noise resistance and generalization ability reaches 99.54%.