DiffDA-Net: diffusion-augmented domain adaptive network for analog circuit fault diagnosis under imbalanced and variable operating conditions.
TL;DR
DiffDA-Net is proposed, 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, and performs on par with SMOTE and ADASYN yet markedly better than GAN-based generation.
Abstract
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%→25% parametric deviation), the dual-alignment domain-adaptation module reaches [Formula: see text] target accuracy under an oracle target-model-selection protocol, exceeding the strongest single-mechanism baseline by 21.79 percentage points; under a fully label-free source-validation protocol it still attains roughly twice the accuracy of the no-adaptation baseline. In the joint imbalanced and cross-domain setting ([Formula: see text], 10 seeds), the full framework attains [Formula: see text] accuracy. Controlled experiments isolate the contribution of each module: removing domain alignment causes the largest degradation ([Formula: see text] pp, [Formula: see text]), identifying dual-alignment domain adaptation as the dominant performance driver, while diffusion augmentation contributes a further [Formula: see text] pp ([Formula: see text]) and performs on par with SMOTE and ADASYN yet markedly better than GAN-based generation. All reported accuracies under the oracle protocol are explicitly labelled as upper-bound estimates.