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To solve the analog fault diagnosis problem with fewer samples, a transformer based auxiliary classifier generative adversarial network (ACGAN) is investigated for circuit fault diagnosis by constructing both generator and discriminator in ACGAN with pure transformer components. The transformer has high model generality due to its weak inductive bias, but it also increases the risk of overfitting on small datasets. Therefore, we use ACGAN to generate sample data to enrich the dataset and mitigate the overfitting. However, ACGAN is severely unstable during the training period, for this reason, a confidence mechanism and a new layer normalization method for the generator is studied to avoid the loss of conditional information. Take the sallen-key filter circuit and the biquad high-pass filter circuit as the experiment objects. The experiment results indicate that the transformer based ACGAN diagnosis method can effectively improve diagnosis accuracy, reach 96.22% and 95.35%, respectively.

An Auxiliary Classifier Generative Adversarial Network based Fault Diagnosis for Analog Circuit