Fault Diagnosis of Analog Circuits Based on Information Fusion in the Time-Frequency Domain

Abstract

To address the low fault feature extraction capability in analog circuits for component classification in analog circuits. Convolutional Block Attention Module-multiple-convolutional neural networks (CBAM-MIL-CNN) is proposed. The model has a better comprehensive performance in fault diagnosis experiments for circuits with secondary four-operator dual second-order low-pass filters, and can effectively achieve efficient classification and localization of all faults.