融合深度可分离小卷积核和CBAM的改进CNN故障诊断模型

Fusion depth separable small convolution kernel and CBAM improved CNN fault diagnosis model

为了解决最大池化丢失信息和平均池化模糊特征的问题,同时提高模型时频图像识别效率,降低模型复杂度,提出一种采用深度可分离小卷积核进行降采样和CBAM的CNN网络模型对轴承进行故障诊断。首先,在除最后一层的池化层中,使用深度可分离小卷积层代替池化层,实现池化层的降采样功能。其次,在最后一层池化层引入CBAM,对时频图像所表征的故障特征给予更多的关注,以提高模型计算效率。再次,使用全局平均池化代替传统全连接层,进一步减少模型参数数量。最后,利用CWRU轴承振动数据和自制实验平台数据验证所提方法在滚动轴承故障诊断方面的有效性和可行性。实验结果表明,融合深度可分离小卷积核和CBAM改进的CNN模型有效减少了模型需要的训练参数和计算量,且在识别准确率方面取得了更优的性能。

In order to solve the problem of maximum pooling loss of information and average pooling fuzzy features, improve the time-frequency image recognition efficiency of the model and reduce the model complexity, A CNN network model using a deep detachable small convolutional kernel for down-sampling and CBAM is proposed for fault diagnosis of bearings. Firstly, in the pooling layer except the last layer, the depth separable small convolution layer is used to replace the pooling layer to realize the down-sampling function of the pooling layer. Secondly, CBAM is introduced in the last pooling layer to pay more attention to the fault features represented by time-frequency images to improve the computational efficiency of the model. Thirdly, global average pooling is used instead of traditional full connection layer to further reduce the number of model parameters. Finally, CWRU bearing vibration data and self-made experimental platform data were used to verify the validity and feasibility of the proposed method in rolling bearing fault diagnosis. Experimental results show that the fusion depth separable small convolution kernel and CBAM improved CNN model can effectively reduce the training parameters and computation required by the model, and achieve better performance in recognition accuracy.