Research on Analog Circuit Fault Diagnosis Based on CBAM-CNN

Abstract

The difficulty in extracting the fault features of analog circuit leads to complex calculation and poor precision with the model. A fault diagnosis method for analog circuits based on attention mechanism and convolutional neural network (CBAM -CNN) is proposed. Firstly, the image features of the input layer were extracted by using the convolution kernel. Followed by rectifying linear unit (ReLU) was connected behind each convolution layer, and a batch normalization (BN) layer was 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) was added after the batch normalization layer to extract the important features. After CBAM, 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.