基于CBAM-CNN的模拟电路故障诊断

针对模拟电路的故障特征难以提取,导致模型计算量复杂、诊断准确率不够高的问题,提出一种基于注意力机制和卷积神经网络(CBAM-CNN)的模拟电路故障诊断方法.首先,利用卷积核提取输入层的图片特征,同时在每个卷积层后面连接一个矫正线性单元(ReLU),并添加批归一化层(BN)解决内部协变量偏移的问题,以提高非线性模型表达能力;然后,在批归一化层后添加注意力机制模块(CBAM),提取重要的特征后连接池化层,降低网络计算复杂度,提高网络的准确率与效率;最后,以Sallen-Key低通滤波器和二级四运放双二阶低通滤波器为研究对象进行故障诊断实验验证.结果表明,所提出方法能够有效提升诊断精度,实现所有故障的高难分类与定位.

CBAM-CNN based analog circuit fault diagnosis

The difficulty in extracting the fault features of analog circuits leads to complex calculation and poor precision with the model. A fault diagnosis method for analog circuits based on the attention mechanism and the convolutional neural network(CBAM-CNN) is proposed. Firstly, the image features of the input layer are extracted by using the convolution kernel, and a rectifying linear unit (ReLU) is connected behind each convolution layer, and a batch normalization (BN) layer is added to solve the problem of internal covariate migration, so as to improve the expression ability of the nonlinear model. Secondly, the convolutional block attention module (CBAM) is added after the batch normalization layer to extract the important features. After that, the pooling layer is connected to reduce the computational complexity of the network and improve the accuracy and efficiency of the network. Finally, the Sallen-Key low-pass filter and the two-stage four-op amplifier double-order low-pass filter are taken as the research objects. The results of fault diagnosis experiments demonstrate that the proposed method can effectively improve the diagnosis accuracy and realize the classification and location of all faults with high difficulty.