DiffDA-Net: diffusion-augmented domain adaptive network for analog circuit fault diagnosis under imbalanced and variable operating conditions
摘要
Accurate fault diagnosis of analog circuits is critical for ensuring the reliability of modern electronic systems. Two practical challenges hinder data-driven methods: data imbalance, where certain fault modes are rare, and variable operating conditions, where models trained under one fault severity fail to generalize to different severity levels. Although both challenges have been studied in isolation, their co-occurrence in analog-circuit diagnosis has received little systematic attention. This paper proposes DiffDA-Net, a unified two-stage framework that addresses both challenges simultaneously, and reports a systematic benchmark of generative augmentation and domain-adaptation components under this joint setting. In the first stage, a conditional Denoising Diffusion Probabilistic Model generates representative class-conditioned fault signals to balance the training dataset. In the second stage, a domain adaptive network couples adversarial training with Maximum Mean Discrepancy regularization to transfer diagnostic knowledge across operating conditions. On a challenging 13-class cross-severity transfer task (50%