A Transfer Learning Method for Fault Diagnosis of Analog Circuit Using Deep Subdomain Adaptation Network

Most data-driven fault diagnosis methods for analog circuits achieve good results when the data satisfies the assumption of independent and equal distribution, which is difficult to achieve in real-world scenarios. To solve this problem, a fault diagnosis method for analog circuits based on Deep Subdomain Adaptation Network is presented. By incorporating the optimization of Local Maximum Mean Discrepancy loss into the training of One-dimensional Convolutional Neural Network, this method can adaptively align the feature representation of the source and target domains without labeling in the target domain. The simulation experiments of Sallen-Key band-pass filter and four-opamp biquad high-pass filter are designed. Two groups of different component parameters are selected as the data sources of source domain and target domain, noise and random offset are added to the target domain data to simulate the actual scene. Through comparative experiments, it is verified that the analog circuit fault diagnosis method presented in this paper has steady training and high accuracy.